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Fix coding style.
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@zheng-da
zheng-da committed Nov 30, 2017
1 parent d9e706a commit f49c23e
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Showing 19 changed files with 162 additions and 153 deletions.
16 changes: 8 additions & 8 deletions src/operator/nn/activation-inl.h
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Original file line number Diff line number Diff line change
Expand Up @@ -101,10 +101,10 @@ void ActivationBackward(const OpContext &ctx, const TBlob &out_grad,

template<typename xpu>
void ActivationCompute(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const OpContext& ctx,
const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
CHECK_EQ(inputs.size(), 1U);
CHECK_EQ(outputs.size(), 1U);
const ActivationParam& param = nnvm::get<ActivationParam>(attrs.parsed);
Expand Down Expand Up @@ -134,10 +134,10 @@ void ActivationCompute(const nnvm::NodeAttrs& attrs,

template<typename xpu>
void ActivationGradCompute(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const OpContext& ctx,
const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
#if MXNET_USE_CUDNN == 1
CHECK_EQ(inputs.size(), 3U);
#else
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32 changes: 16 additions & 16 deletions src/operator/nn/batch_norm-inl.h
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Original file line number Diff line number Diff line change
Expand Up @@ -110,10 +110,10 @@ class BatchNormOp {
* \sa OpReqType, OpContext
*/
void Forward(const OpContext &ctx,
const std::vector<TBlob> &in_data,
const std::vector<OpReqType> &req,
const std::vector<TBlob> &out_data,
const std::vector<TBlob> &aux_states) {
const std::vector<TBlob> &in_data,
const std::vector<OpReqType> &req,
const std::vector<TBlob> &out_data,
const std::vector<TBlob> &aux_states) {
using namespace mshadow;
using namespace mshadow::expr;

Expand Down Expand Up @@ -160,12 +160,12 @@ class BatchNormOp {
* \sa OperatorProperty, OpReqType, OpContext
*/
void Backward(const OpContext &ctx,
const std::vector<TBlob> &out_grad,
const std::vector<TBlob> &in_data,
const std::vector<TBlob> &out_data,
const std::vector<OpReqType> &req,
const std::vector<TBlob> &in_grad,
const std::vector<TBlob> &aux_states) {
const std::vector<TBlob> &out_grad,
const std::vector<TBlob> &in_data,
const std::vector<TBlob> &out_data,
const std::vector<OpReqType> &req,
const std::vector<TBlob> &in_grad,
const std::vector<TBlob> &aux_states) {
CHECK_EQ(out_grad.size(), param_.output_mean_var ? 3U : 1U);
CHECK_EQ(in_data.size(), 3U);
CHECK_EQ(out_data.size(), 3U);
Expand Down Expand Up @@ -222,9 +222,9 @@ static BatchNormOp<xpu, DType, AccReal> &GetBatchNormOp(const BatchNormParam& pa

template<typename xpu>
void BatchNormCompute(const nnvm::NodeAttrs& attrs,
const OpContext& ctx, const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const OpContext& ctx, const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const BatchNormParam& param = nnvm::get<BatchNormParam>(attrs.parsed);
CHECK_EQ(inputs.size(), 5U);
std::vector<TBlob> in_data(inputs.begin(), inputs.begin() + 3);
Expand All @@ -237,9 +237,9 @@ void BatchNormCompute(const nnvm::NodeAttrs& attrs,

template<typename xpu>
void BatchNormGradCompute(const nnvm::NodeAttrs& attrs,
const OpContext& ctx, const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const OpContext& ctx, const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
CHECK_EQ(inputs.size(), 11U);
const BatchNormParam& param = nnvm::get<BatchNormParam>(attrs.parsed);
std::vector<TBlob> out_grad(inputs.begin(),
Expand Down
5 changes: 3 additions & 2 deletions src/operator/nn/batch_norm.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -318,7 +318,8 @@ void BatchNormOp<xpu, DType, AccReal>::DoBackward(mshadow::Stream<cpu> *,
DMLC_REGISTER_PARAMETER(BatchNormParam);

static bool BatchNormShape(const nnvm::NodeAttrs& attrs,
std::vector<TShape> *in_shape, std::vector<TShape> *out_shape) {
std::vector<TShape> *in_shape,
std::vector<TShape> *out_shape) {
const BatchNormParam& param = nnvm::get<BatchNormParam>(attrs.parsed);
using namespace mshadow;
CHECK_EQ(in_shape->size(), 5U) << "Input:[data, gamma, beta, MovingMean, MovingVar]";
Expand Down Expand Up @@ -357,7 +358,7 @@ static inline std::vector<std::string> ListOutputs() {
}

static bool BatchNormType(const nnvm::NodeAttrs& attrs,
std::vector<int> *in_type, std::vector<int> *out_type) {
std::vector<int> *in_type, std::vector<int> *out_type) {
using namespace mshadow;
CHECK_GE(in_type->size(), 1U);
const int dtype = (*in_type)[0];
Expand Down
26 changes: 13 additions & 13 deletions src/operator/nn/convolution-inl.h
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Original file line number Diff line number Diff line change
Expand Up @@ -161,9 +161,9 @@ class ConvolutionOp {
}

void Forward(const OpContext &ctx,
const std::vector<TBlob> &in_data,
const std::vector<OpReqType> &req,
const std::vector<TBlob> &out_data) {
const std::vector<TBlob> &in_data,
const std::vector<OpReqType> &req,
const std::vector<TBlob> &out_data) {
using namespace mshadow;
using namespace mshadow::expr;
CHECK_EQ(req[conv::kOut], kWriteTo);
Expand Down Expand Up @@ -233,10 +233,10 @@ class ConvolutionOp {
}

void Backward(const OpContext &ctx,
const std::vector<TBlob>& out_grad,
const std::vector<TBlob>& in_data,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& in_grad) {
const std::vector<TBlob>& out_grad,
const std::vector<TBlob>& in_data,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& in_grad) {
using namespace mshadow;
using namespace mshadow::expr;
CHECK_EQ(out_grad.size(), 1U);
Expand Down Expand Up @@ -387,9 +387,9 @@ class ConvolutionOp {

template<typename xpu>
void ConvolutionCompute(const nnvm::NodeAttrs& attrs,
const OpContext& ctx, const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const OpContext& ctx, const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const ConvolutionParam& param = nnvm::get<ConvolutionParam>(attrs.parsed);
MSHADOW_REAL_TYPE_SWITCH(inputs[conv::kData].type_flag_, DType, {
static thread_local ConvolutionOp<xpu, DType> op;
Expand All @@ -400,9 +400,9 @@ void ConvolutionCompute(const nnvm::NodeAttrs& attrs,

template<typename xpu>
void ConvolutionGradCompute(const nnvm::NodeAttrs& attrs,
const OpContext& ctx, const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const OpContext& ctx, const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const ConvolutionParam& param = nnvm::get<ConvolutionParam>(attrs.parsed);
std::vector<TBlob> in_data(inputs.begin() + 1, inputs.end());
const TBlob &out_grad = inputs[0];
Expand Down
5 changes: 3 additions & 2 deletions src/operator/nn/convolution.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -52,7 +52,8 @@ static inline std::vector<std::string> ListArguments(const ConvolutionParam& par
}

static bool ConvolutionShape(const nnvm::NodeAttrs& attrs,
std::vector<TShape> *in_shape, std::vector<TShape> *out_shape) {
std::vector<TShape> *in_shape,
std::vector<TShape> *out_shape) {
using namespace mshadow;
const ConvolutionParam& param_ = nnvm::get<ConvolutionParam>(attrs.parsed);
if (!param_.no_bias) {
Expand Down Expand Up @@ -241,7 +242,7 @@ static bool ConvolutionShape(const nnvm::NodeAttrs& attrs,
}

static bool ConvolutionType(const nnvm::NodeAttrs& attrs,
std::vector<int> *in_type, std::vector<int> *out_type) {
std::vector<int> *in_type, std::vector<int> *out_type) {
const ConvolutionParam& param_ = nnvm::get<ConvolutionParam>(attrs.parsed);
CHECK_GE(in_type->size(), 1U);
int dtype = (*in_type)[0];
Expand Down
38 changes: 19 additions & 19 deletions src/operator/nn/deconvolution-inl.h
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Original file line number Diff line number Diff line change
Expand Up @@ -202,9 +202,9 @@ class DeconvolutionOp {
}

void Forward(const OpContext &ctx,
const std::vector<TBlob> &in_data,
const std::vector<OpReqType> &req,
const std::vector<TBlob> &out_data) {
const std::vector<TBlob> &in_data,
const std::vector<OpReqType> &req,
const std::vector<TBlob> &out_data) {
using namespace mshadow;
using namespace mshadow::expr;

Expand Down Expand Up @@ -309,10 +309,10 @@ class DeconvolutionOp {
}

void Backward(const OpContext &ctx,
const std::vector<TBlob> &out_grad,
const std::vector<TBlob> &in_data,
const std::vector<OpReqType> &req,
const std::vector<TBlob> &in_grad) {
const std::vector<TBlob> &out_grad,
const std::vector<TBlob> &in_data,
const std::vector<OpReqType> &req,
const std::vector<TBlob> &in_grad) {
using namespace mshadow;
using namespace mshadow::expr;
// TODO(bing): check the BLAS Handle, be careful
Expand Down Expand Up @@ -453,9 +453,9 @@ class DeconvolutionOp {

template<typename xpu>
void _DeconvolutionCompute(const DeconvolutionParam& param,
const OpContext& ctx, const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const OpContext& ctx, const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
MSHADOW_REAL_TYPE_SWITCH(inputs[deconv::kData].type_flag_, DType, {
static thread_local DeconvolutionOp<xpu, DType> op;
op.Init(param);
Expand All @@ -465,18 +465,18 @@ void _DeconvolutionCompute(const DeconvolutionParam& param,

template<typename xpu>
void DeconvolutionCompute(const nnvm::NodeAttrs& attrs,
const OpContext& ctx, const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const OpContext& ctx, const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const DeconvolutionParam& param = nnvm::get<DeconvolutionParam>(attrs.parsed);
_DeconvolutionCompute<xpu>(param, ctx, inputs, req, outputs);
}

template<typename xpu>
void _DeconvolutionGradCompute(const DeconvolutionParam& param,
const OpContext& ctx, const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const OpContext& ctx, const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
std::vector<TBlob> in_data(inputs.begin() + 1, inputs.end());
const TBlob &out_grad = inputs[0];
const std::vector<TBlob> &in_grad = outputs;
Expand All @@ -491,9 +491,9 @@ void _DeconvolutionGradCompute(const DeconvolutionParam& param,

template<typename xpu>
void DeconvolutionGradCompute(const nnvm::NodeAttrs& attrs,
const OpContext& ctx, const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const OpContext& ctx, const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const DeconvolutionParam& param = nnvm::get<DeconvolutionParam>(attrs.parsed);
_DeconvolutionGradCompute<xpu>(param, ctx, inputs, req, outputs);
}
Expand Down
5 changes: 3 additions & 2 deletions src/operator/nn/deconvolution.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,8 @@ namespace mxnet {
namespace op {

static bool DeconvolutionShape(const nnvm::NodeAttrs& attrs,
std::vector<TShape> *in_shape, std::vector<TShape> *out_shape) {
std::vector<TShape> *in_shape,
std::vector<TShape> *out_shape) {
const DeconvolutionParam& param_ = nnvm::get<DeconvolutionParam>(attrs.parsed);
#if MXNET_USE_CUDNN == 0
if (param_.kernel.ndim() != 2) {
Expand Down Expand Up @@ -236,7 +237,7 @@ static inline std::vector<std::string> ListArguments(const DeconvolutionParam& p
}

static bool DeconvolutionType(const nnvm::NodeAttrs& attrs,
std::vector<int> *in_type, std::vector<int> *out_type) {
std::vector<int> *in_type, std::vector<int> *out_type) {
const DeconvolutionParam& param_ = nnvm::get<DeconvolutionParam>(attrs.parsed);
CHECK_GE(in_type->size(), 1U);
int dtype = (*in_type)[0];
Expand Down
22 changes: 13 additions & 9 deletions src/operator/nn/deconvolution.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -41,9 +41,11 @@ static DeconvolutionOp<gpu, DType> &get_op(const DeconvolutionParam& param) {

template<typename DType>
static CuDNNDeconvolutionOp<DType> &get_cudnn_op(const DeconvolutionParam& param,
int forward_compute_type, int backward_compute_type,
const std::vector<TShape>& in_shape, const std::vector<TShape>& out_shape,
const Context& ctx, bool backward) {
int forward_compute_type,
int backward_compute_type,
const std::vector<TShape>& in_shape,
const std::vector<TShape>& out_shape,
const Context& ctx, bool backward) {
// Convolution forward has to be called before backward for this operator.
// So we can't make this operator thread local. backward might be called
// in another thread.
Expand All @@ -55,9 +57,10 @@ static CuDNNDeconvolutionOp<DType> &get_cudnn_op(const DeconvolutionParam& param

template<>
void DeconvolutionCompute<gpu>(const nnvm::NodeAttrs& attrs,
const OpContext& ctx, const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const OpContext& ctx,
const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const DeconvolutionParam& param = nnvm::get<DeconvolutionParam>(attrs.parsed);
int dtype = inputs[0].type_flag_;
// If 1D deconvolution, use MXNet implementation
Expand Down Expand Up @@ -98,9 +101,10 @@ void DeconvolutionCompute<gpu>(const nnvm::NodeAttrs& attrs,

template<>
void DeconvolutionGradCompute<gpu>(const nnvm::NodeAttrs& attrs,
const OpContext& ctx, const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const OpContext& ctx,
const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const DeconvolutionParam& param = nnvm::get<DeconvolutionParam>(attrs.parsed);
std::vector<TBlob> in_data(inputs.begin() + 1, inputs.end());
const TBlob &out_grad = inputs[0];
Expand Down
21 changes: 11 additions & 10 deletions src/operator/nn/dropout-inl.h
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Original file line number Diff line number Diff line change
Expand Up @@ -101,7 +101,7 @@ class DropoutOp {
}

void Forward(const OpContext &ctx, const std::vector<TBlob> &in_data,
const std::vector<OpReqType> &req, const std::vector<TBlob> &out_data) {
const std::vector<OpReqType> &req, const std::vector<TBlob> &out_data) {
using namespace mshadow;
using namespace mshadow::expr;
CHECK_EQ(in_data.size(), 1U);
Expand Down Expand Up @@ -136,7 +136,8 @@ class DropoutOp {
}

void Backward(const OpContext &ctx, const TBlob &out_grad,
const TBlob &out_data_mask, const OpReqType &req, const TBlob &in_grad) {
const TBlob &out_data_mask, const OpReqType &req,
const TBlob &in_grad) {
using namespace mshadow;
using namespace mshadow::expr;
Stream<xpu> *s = ctx.get_stream<xpu>();
Expand Down Expand Up @@ -169,10 +170,10 @@ class DropoutOp {

template<typename xpu>
void DropoutCompute(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const OpContext& ctx,
const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const DropoutParam& param = nnvm::get<DropoutParam>(attrs.parsed);
MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
static thread_local DropoutOp<xpu, DType> op;
Expand All @@ -183,10 +184,10 @@ void DropoutCompute(const nnvm::NodeAttrs& attrs,

template<typename xpu>
void DropoutGradCompute(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const OpContext& ctx,
const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const DropoutParam& param = nnvm::get<DropoutParam>(attrs.parsed);
CHECK_EQ(inputs.size(), 2U);
CHECK_EQ(outputs.size(), 1);
Expand Down
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