mirror of
https://github.com/zebrajr/pytorch.git
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6248266d91
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17862 Differential Revision: D14429234 Pulled By: bddppq fbshipit-source-id: 5cb8750bd9db0ff8a179977d2bfbb180265cce81
117 lines
3.1 KiB
Plaintext
117 lines
3.1 KiB
Plaintext
/**
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* Copyright (c) 2016-present, Facebook, Inc.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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/* SampleAs by Kaiming He for Mask R-CNN
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X.dim32(0) = L.dim32(0)
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Y's output samples are the samples of X for which L > 0.
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*/
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#include <cfloat>
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#include "caffe2/core/context_gpu.h"
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#include "modules/detectron/sample_as_op.h"
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#include <stdio.h>
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namespace caffe2 {
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template <>
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bool SampleAsOp<float, CUDAContext>::RunOnDevice() {
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auto& X = Input(0); // Input data to be sliced
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auto& L = Input(1); // Target data that provide the identity
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CAFFE_ENFORCE(
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X.dim32(0) == L.dim32(0),
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"X.dim32(0) must be equal to L.dim32(0)",
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"(",
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X.dim32(0),
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" vs. ",
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L.dim32(0),
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")");
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// copy L to CPU:
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std::vector<int> labels(L.dim32(0));
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context_.CopyBytes<CUDAContext, CPUContext>(
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L.dim32(0) * sizeof(int), L.data<int>(), &labels[0]);
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// Make sure that the copy is finished
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context_.FinishDeviceComputation();
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int count = 0;
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for (int i = 0; i < L.dim32(0); i++) {
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if (labels[i] > 0) {
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count++;
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}
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}
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assert(count > 0);
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// resize Y
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vector<int64_t> out_shape(X.sizes().vec());
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out_shape[0] = count;
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auto* Y = Output(0, out_shape, at::dtype<float>()); // Sliced data (Y.dim32(0) = num of (L > 0))
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const int len = X.size() / X.dim32(0);
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float* output = Y->mutable_data<float>();
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for (int i = 0; i < L.dim32(0); i++) {
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if (labels[i] > 0) {
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context_.CopyBytes<CUDAContext, CUDAContext>(
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len * sizeof(float), X.data<float>() + i * len, output);
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output += len;
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} // if
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} // i
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return true;
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}
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template <>
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bool SampleAsGradientOp<float, CUDAContext>::RunOnDevice() {
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auto& X = Input(0);
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auto& L = Input(1);
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auto& dY = Input(2);
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auto* dX = Output(0, X.sizes(), at::dtype<float>());
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// copy L to CPU:
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std::vector<int> labels(L.dim32(0));
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context_.CopyBytes<CUDAContext, CPUContext>(
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L.dim32(0) * sizeof(int), L.data<int>(), &labels[0]);
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// Make sure that the copy is finished
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context_.FinishDeviceComputation();
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// zero-out dX
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math::Set<float, CUDAContext>(
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dX->size(), 0.f, dX->mutable_data<float>(), &context_);
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const int len = X.size() / X.dim32(0);
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const float* input = dY.data<float>();
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for (int i = 0; i < L.dim32(0); i++) {
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if (labels[i] > 0) {
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context_.CopyBytes<CUDAContext, CUDAContext>(
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len * sizeof(float), input, dX->mutable_data<float>() + i * len);
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input += len;
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} // if
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} // i
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return true;
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}
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REGISTER_CUDA_OPERATOR(SampleAs, SampleAsOp<float, CUDAContext>);
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REGISTER_CUDA_OPERATOR(
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SampleAsGradient,
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SampleAsGradientOp<float, CUDAContext>);
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} // namespace caffe2
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