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1433160a36
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/66742 Modified loops in files under fbsource/fbcode/caffe2/ from the format `for(TYPE var=x0;var<x_max;x++)` to the format `for(const auto var: irange(xmax))` This was achieved by running r-barnes's loop upgrader script (D28874212) with some modification to exclude all files under /torch/jit and a number of reversions or unused variable suppression warnings added by hand. Test Plan: Sandcastle Reviewed By: malfet Differential Revision: D31705366 fbshipit-source-id: be58222426c192406a7f93c21582c3f6f2082401
93 lines
2.1 KiB
C++
93 lines
2.1 KiB
C++
#ifndef CAFFE2_OPERATORS_ORDER_SWITCH_OPS_H_
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#define CAFFE2_OPERATORS_ORDER_SWITCH_OPS_H_
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#include "caffe2/core/operator.h"
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#include "caffe2/utils/math.h"
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#include <c10/util/irange.h>
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#include <vector>
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namespace caffe2 {
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// Note(Yangqing): I think it is possible to do a more general swapaxes operator
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// but I am a little afraid of going down that general path. Only implementing
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// the two actually needed ones here.
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template <typename T, class Context>
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class NHWC2NCHWOp final : public Operator<Context> {
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public:
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USE_OPERATOR_CONTEXT_FUNCTIONS;
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USE_SIMPLE_CTOR_DTOR(NHWC2NCHWOp);
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bool RunOnDevice() override {
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const auto& X = Input(0);
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const int ndim = X.dim();
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CAFFE_ENFORCE_GE(ndim, 3);
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const int N = X.dim32(0);
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const int C = X.dim32(ndim - 1);
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std::vector<int64_t> Y_dims(ndim);
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Y_dims[0] = N;
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Y_dims[1] = C;
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int HxW = 1;
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for (const auto i : c10::irange(2, ndim)) {
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Y_dims[i] = X.dim32(i - 1);
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HxW *= Y_dims[i];
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}
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auto* Y = Output(0, Y_dims, at::dtype<T>());
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if (X.numel() <= 0) {
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return true;
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}
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math::NHWC2NCHW<T, Context>(
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N,
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C,
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HxW,
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X.template data<T>(),
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Y->template mutable_data<T>(),
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&context_);
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return true;
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}
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};
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template <typename T, class Context>
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class NCHW2NHWCOp final : public Operator<Context> {
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public:
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USE_OPERATOR_CONTEXT_FUNCTIONS;
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USE_SIMPLE_CTOR_DTOR(NCHW2NHWCOp);
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bool RunOnDevice() override {
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const auto& X = Input(0);
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const int ndim = X.dim();
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CAFFE_ENFORCE_GE(ndim, 3);
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const int N = X.dim32(0);
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const int C = X.dim32(1);
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std::vector<int64_t> Y_dims(ndim);
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Y_dims[0] = N;
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Y_dims[ndim - 1] = C;
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int HxW = 1;
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for (int i = 1; i < ndim - 1; ++i) {
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Y_dims[i] = X.dim32(i + 1);
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HxW *= Y_dims[i];
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}
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auto* Y = Output(0, Y_dims, at::dtype<T>());
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if (X.numel() <= 0) {
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return true;
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}
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math::NCHW2NHWC<T, Context>(
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N,
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C,
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HxW,
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X.template data<T>(),
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Y->template mutable_data<T>(),
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&context_);
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return true;
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}
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};
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} // namespace caffe2
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#endif // CAFFE2_OPERATORS_ORDER_SWITCH_OPS_H_
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