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4f34cd6d1e
Avoid exposing defines that conflict with google logging, since this blocks external usage of libtorch in certain cases. All the 'interesting' changes should be in these two files, and the rest should just be mechanical changes via sed. c10/util/logging_is_not_google_glog.h c10/util/logging_is_google_glog.h Fixes https://github.com/pytorch/pytorch/issues/81415 cc @miladm @malfet Pull Request resolved: https://github.com/pytorch/pytorch/pull/82032 Approved by: https://github.com/soumith, https://github.com/miladm
107 lines
2.8 KiB
C++
107 lines
2.8 KiB
C++
/**
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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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#ifndef UPSAMPLE_NEAREST_OP_H_
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#define UPSAMPLE_NEAREST_OP_H_
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#include "caffe2/core/context.h"
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#include "caffe2/core/logging.h"
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#include "caffe2/core/operator.h"
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#include "caffe2/utils/math.h"
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namespace caffe2 {
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template <typename T, class Context>
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class UpsampleNearestOp final : public Operator<Context> {
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public:
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UpsampleNearestOp(const OperatorDef& operator_def, Workspace* ws)
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: Operator<Context>(operator_def, ws),
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scale_(this->template GetSingleArgument<int>("scale", 2)) {
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TORCH_DCHECK_GE(scale_, 1);
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}
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USE_OPERATOR_CONTEXT_FUNCTIONS;
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bool RunOnDevice() override {
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auto& X = Input(0);
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auto out_shape = X.sizes().vec();
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out_shape[X.dim() - 1] *= scale_;
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out_shape[X.dim() - 2] *= scale_;
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auto* Y = Output(0, out_shape, at::dtype<T>());
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int d1;
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int d2;
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int d3;
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if (X.dim() == 3) {
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d1 = Y->dim32(0);
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d2 = Y->dim32(1);
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d3 = Y->dim32(2);
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} else {
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d1 = Y->dim32(0) * Y->dim32(1);
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d2 = Y->dim32(2);
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d3 = Y->dim32(3);
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}
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const T *input_data = X.template data<T>();
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T *output_data = Y->template mutable_data<T>();
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int scaled_d2 = d2 / scale_;
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int scaled_d3 = d3 / scale_;
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#ifdef _OPENMP
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#pragma omp parallel for
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#endif
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for (int i = 0; i < d1; ++i) {
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for (int j = 0; j < d2; ++j) {
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for (int u = 0; u < d3; ++u) {
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int ii = (i * d2 + j) * d3 + u;
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int scaled_u = u / scale_;
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int scaled_j = j / scale_;
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int ipidx = ((i * scaled_d2) + scaled_j) * scaled_d3 + scaled_u;
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output_data[ii] = input_data[ipidx];
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}
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}
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}
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return true;
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}
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protected:
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int scale_;
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};
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template <typename T, class Context>
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class UpsampleNearestGradientOp final : public Operator<Context> {
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public:
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UpsampleNearestGradientOp(const OperatorDef& def, Workspace* ws)
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: Operator<Context>(def, ws),
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scale_(this->template GetSingleArgument<int>("scale", 2)) {
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TORCH_DCHECK_GE(scale_, 1);
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}
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USE_OPERATOR_CONTEXT_FUNCTIONS;
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bool RunOnDevice() override {
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// No CPU implementation for now
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CAFFE_NOT_IMPLEMENTED;
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}
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protected:
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int scale_;
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};
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} // namespace caffe2
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#endif // UPSAMPLE_NEAREST_OP_H_
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