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Tensor construction codemod(ResizeLike) - 3/7 (#15122)

Browse Source 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15122

Codemod generated with clangr shard mode, 25 files per diff,
motivation: https://github.com/pytorch/pytorch/pull/12407

Reviewed By: dzhulgakov

Differential Revision: D13419643

fbshipit-source-id: 65b5a037b94d458b944d51f790ba2829db1fb530
This commit is contained in:
Jerry Zhang 2018-12-14 02:05:15 -08:00 committed by Facebook Github Bot
parent 78bf1a9065
commit fb8487d708
10 changed files with 35 additions and 44 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
caffe2/mpi/mpi_ops.h
View File

@ -70,8 +70,7 @@ class MPIReduceOp final : public Operator<Context> {
bool RunOnDevice() override {
MPI_Comm comm = OperatorBase::Input<MPICommonWorldWrapper>(0).comm();
auto& input = Input(1);
auto* output = Output(0);
output->ResizeLike(input);
auto* output = Output(0, input.sizes(), at::dtype<T>());
MPI_CHECK(MPI_Reduce(
const_cast<T*>(input.template data<T>()),
output->template mutable_data<T>(),
@ -123,8 +122,7 @@ class MPIAllreduceOp final : public Operator<Context> {
bool RunOnDevice() override {
MPI_Comm comm = OperatorBase::Input<MPICommonWorldWrapper>(0).comm();
auto& input = Input(1);
auto* output = Output(0);
output->ResizeLike(input);
auto* output = Output(0, input.sizes(), at::dtype<T>());
void* source;
if (output->template mutable_data<T>() == input.template data<T>()) {
// We are doing in-place call. Special case handling.

8
caffe2/operators/activation_ops_cudnn.h
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@ -68,8 +68,8 @@ class CuDNNActivationOp final : public CuDNNActivationOpBase {
template <typename T>
bool DoRunWithType() {
const auto& X = Input(0);
auto* Y = Output(0);
Y->ResizeLike(X);
auto* Y = Output(0, X.sizes(), at::dtype<T>());
if (X.numel() == 0) {
Y->template mutable_data<T>();
return true;
@ -107,8 +107,8 @@ class CuDNNActivationGradientOp final : public CuDNNActivationOpBase {
bool DoRunWithType() {
const auto& Y = Input(0);
const auto& dY = Input(1);
auto* dX = Output(0);
dX->ResizeLike(Y);
auto* dX = Output(0, Y.sizes(), at::dtype<T>());
if (Y.numel() == 0) {
dX->template mutable_data<T>();
return true;

22
caffe2/operators/affine_channel_op.cc
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@ -54,8 +54,8 @@ template <>
bool AffineChannelGradientOp<float, CPUContext>::RunOnDeviceWithOrderNCHW() {
const auto& dY = Input(0);
const auto& scale = is_learnable_ ? Input(2) : Input(1);
auto* dX = Output(0);
dX->ResizeLike(dY);
auto* dX = Output(0, dY.sizes(), at::dtype<float>());
const int N = dY.dim32(0);
const int C = dY.dim32(1);
const int HxW = dY.numel() / (N * C);
@ -75,10 +75,9 @@ bool AffineChannelGradientOp<float, CPUContext>::RunOnDeviceWithOrderNCHW() {
if (is_learnable_) {
const auto& X = Input(1);
const float* X_data = X.data<float>();
auto* dscale = Output(1);
auto* dbias = Output(2);
dscale->ResizeLike(scale);
dbias->ResizeLike(scale);
auto* dscale = Output(1, scale.sizes(), at::dtype<float>());
auto* dbias = Output(2, scale.sizes(), at::dtype<float>());
AffineChannelScaleBiasBackwardNCHW<float>(
N,
C,
@ -95,8 +94,8 @@ template <>
bool AffineChannelGradientOp<float, CPUContext>::RunOnDeviceWithOrderNHWC() {
const auto& dY = Input(0);
const auto& scale = is_learnable_ ? Input(2) : Input(1);
auto* dX = Output(0);
dX->ResizeLike(dY);
auto* dX = Output(0, dY.sizes(), at::dtype<float>());
const int ndim = dY.dim();
const int C = dY.dim32(ndim - 1);
const int rows = dY.numel() / C;
@ -115,10 +114,9 @@ bool AffineChannelGradientOp<float, CPUContext>::RunOnDeviceWithOrderNHWC() {
const float* X_data = X.data<float>();
const int N = X.dim32(0);
const int HxW = rows / N;
auto* dscale = Output(1);
auto* dbias = Output(2);
dscale->ResizeLike(scale);
dbias->ResizeLike(scale);
auto* dscale = Output(1, scale.sizes(), at::dtype<float>());
auto* dbias = Output(2, scale.sizes(), at::dtype<float>());
AffineChannelScaleBiasBackwardNHWC<float>(
N,
C,

19
caffe2/operators/affine_channel_op.h
View File

@ -32,18 +32,17 @@ class AffineChannelOp final : public Operator<Context> {
const auto& X = Input(0);
const auto& scale = Input(1);
const auto& bias = Input(2);
auto* Y = Output(0);
if (is_learnable_) {
CAFFE_ENFORCE_NE(
Y,
&X,
CAFFE_ENFORCE(
!IsInputOutputAlias(0, 0),
"In-place affine_channel_op is not supported when "
"is_learnable = true.");
}
const int N = X.dim32(0);
const int C = X.dim32(1);
const int HxW = X.numel() / (N * C);
Y->ResizeLike(X);
auto* Y = Output(0, X.sizes(), at::dtype<T>());
math::AffineChannel<T, Context, StorageOrder::NCHW>(
N,
C,
@ -60,11 +59,10 @@ class AffineChannelOp final : public Operator<Context> {
const auto& X = Input(0);
const auto& scale = Input(1);
const auto& bias = Input(2);
auto* Y = Output(0);
if (is_learnable_) {
CAFFE_ENFORCE_NE(
Y,
&X,
CAFFE_ENFORCE(
!IsInputOutputAlias(0, 0),
"In-place affine_channel_op is not supported when "
"is_learnable = true.");
}
@ -72,7 +70,8 @@ class AffineChannelOp final : public Operator<Context> {
const int N = X.dim32(0);
const int C = X.dim32(ndim - 1);
const int HxW = X.numel() / (N * C);
Y->ResizeLike(X);
auto* Y =
Output(0, X.sizes(), at::dtype<T>());
math::AffineChannel<T, Context, StorageOrder::NHWC>(
N,
C,

3
caffe2/operators/batch_box_cox_op.cc
View File

@ -79,8 +79,7 @@ bool BatchBoxCoxOp<CPUContext>::DoRunWithType() {
auto N = data.size(0);
auto D = data.size_from_dim(1);
auto* output = Output(0);
output->ResizeLike(Input(DATA));
auto* output = Output(0, Input(DATA).sizes(), at::dtype<T>());
auto* output_ptr = output->template mutable_data<T>();
if (data.numel() <= 0) {

3
caffe2/operators/batch_gather_ops.h
View File

@ -58,7 +58,6 @@ class BatchGatherGradientOp final : public Operator<Context> {
auto& data = Input(DATA);
auto& indices = Input(INDICES);
auto& grad = Input(GRAD);
auto* output = Output(0);
// ONNX allows negative axis to index from the back, valid range: [-r, r].
int axis = axis_;
@ -76,7 +75,7 @@ class BatchGatherGradientOp final : public Operator<Context> {
"batch gather outer dimensions should match");
}
output->ResizeLike(data);
auto* output = Output(0, data.sizes(), at::dtype<TData>());
TData* out_data = output->template mutable_data<TData>();
if (data.numel() <= 0) {
return true;

4
caffe2/operators/batch_moments_op.h
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@ -72,12 +72,12 @@ class BatchMomentsGradientOp final : public Operator<Context> {
const auto& dmu = Input(0);
const auto& dvar = Input(1);
const auto& X = Input(2);
auto* dX = Output(0);
const int ndim = X.dim();
const int N = X.dim32(0);
const int C = order_ == StorageOrder::NCHW ? X.dim32(1) : X.dim32(ndim - 1);
const int HxW = X.numel() / (N * C);
dX->ResizeLike(X);
auto* dX = Output(0, X.sizes(), at::dtype<T>());
const T* dmu_data = dmu.template data<T>();
const T* dvar_data = dvar.template data<T>();
const T* X_data = X.template data<T>();

3
caffe2/operators/bbox_transform_op.cc
View File

@ -91,7 +91,6 @@ bool BBoxTransformOp<float, CPUContext>::RunOnDevice() {
const auto& roi_in = Input(0);
const auto& delta_in = Input(1);
const auto& iminfo_in = Input(2);
auto* box_out = Output(0);
const int box_dim = rotated_ ? 5 : 4;
const int N = roi_in.dim32(0);
@ -132,7 +131,7 @@ bool BBoxTransformOp<float, CPUContext>::RunOnDevice() {
Eigen::Map<const ERArrXXf> iminfo(
iminfo_in.data<float>(), iminfo_in.size(0), iminfo_in.size(1));
box_out->ResizeLike(delta_in);
auto* box_out = Output(0, delta_in.sizes(), at::dtype<float>());
Eigen::Map<ERArrXXf> new_boxes(
box_out->template mutable_data<float>(),
box_out->dim32(0),

4
caffe2/operators/bisect_percentile_op.h
View File

@ -64,8 +64,8 @@ class BisectPercentileOp final : public Operator<Context> {
const float* raw_data = raw.template data<float>();
// Output
auto* pct = Output(PCT);
pct->ResizeLike(raw);
auto* pct = Output(PCT, raw.sizes(), at::dtype<float>());
float* pct_output = pct->template mutable_data<float>();
// Compute percentile for each raw feature value

7
caffe2/operators/boolean_mask_ops.cc
View File

@ -20,7 +20,7 @@ class BooleanMaskLengthsOp final : public Operator<Context> {
bool DoRunWithType() {
auto& lengths = Input(0);
auto& mask = Input(1);
auto* lengthsOut = Output(0);
CAFFE_ENFORCE(lengths.dim() == 1);
CAFFE_ENFORCE(mask.dim() == 1);
const auto* lengthsPtr = lengths.template data<T>();
@ -28,7 +28,7 @@ class BooleanMaskLengthsOp final : public Operator<Context> {
auto totalLength =
std::accumulate(lengthsPtr, lengthsPtr + lengths.numel(), 0);
CAFFE_ENFORCE(mask.numel() == totalLength);
lengthsOut->ResizeLike(lengths);
auto* lengthsOut = Output(0, lengths.sizes(), at::dtype<T>());
auto* lengthsOutPtr = lengthsOut->template mutable_data<T>();
int p = 0;
for (int i = 0; i < lengths.numel(); ++i) {
@ -365,8 +365,7 @@ bool SequenceMaskOp<CPUContext>::DoRunWithType() {
window_centers = &Input(1);
}
auto* output = Output(0);
output->ResizeLike(*input);
auto* output = Output(0, input->sizes(), at::dtype<T>());
const auto canonical_axis = input->canonical_axis_index(axis_);