#ifndef CAFFE2_OPERATORS_CONV_OP_H_
|
#define CAFFE2_OPERATORS_CONV_OP_H_
|
|
#include "caffe2/core/context.h"
|
#include "caffe2/core/operator.h"
|
#include "caffe2/operators/conv_op_shared.h"
|
#include "caffe2/operators/conv_pool_op_base.h"
|
|
C10_DECLARE_bool(caffe2_force_shared_col_buffer);
|
|
namespace caffe2 {
|
|
template <typename T, class Context>
|
class ConvOp final : public ConvPoolOpBase<Context> {
|
public:
|
USE_CONV_POOL_BASE_FUNCTIONS(Context);
|
explicit ConvOp(const OperatorDef& operator_def, Workspace* ws)
|
: ConvPoolOpBase<Context>(operator_def, ws) {
|
// Since this is the default convolution implementation, we will
|
// use CAFFE_ENFORCE instead of OPERATOR_NEEDS_FEATURE.
|
CAFFE_ENFORCE(
|
(group_ == 1 || order_ == StorageOrder::NCHW ||
|
std::is_same<Context, CPUContext>::value),
|
"Group convolution only supports NCHW order or CPUContext right now.");
|
|
// Create shared buffer mutex in the constructor
|
// to avoid race-condition in DAGNet.
|
if (FLAGS_caffe2_force_shared_col_buffer || shared_buffer_) {
|
createSharedBuffer<Context>(ws_);
|
}
|
}
|
~ConvOp() {}
|
|
bool RunOnDeviceWithOrderNCHW() override;
|
bool RunOnDeviceWithOrderNHWC() override;
|
|
private:
|
bool Run1x1ConvOnDeviceWithOrderNCHW(
|
const int N,
|
const int C,
|
const int HxW,
|
const int M,
|
const T* X,
|
const T* filter,
|
const T* bias,
|
T* Y);
|
|
bool Run1x1ConvOnDeviceWithOrderNHWC(
|
const int N,
|
const int C,
|
const int HxW,
|
const int M,
|
const T* X,
|
const T* filter,
|
const T* bias,
|
T* Y);
|
|
Tensor col_buffer_{Context::GetDeviceType()};
|
Tensor bias_multiplier_{Context::GetDeviceType()};
|
Tensor img_shape_device_{Context::GetDeviceType()};
|
Tensor col_buffer_shape_device_{Context::GetDeviceType()};
|
// Input: X, W, b
|
// Output: Y
|
INPUT_TAGS(INPUT, FILTER, BIAS);
|
};
|
|
template <typename T, class Context>
|
class ConvGradientOp final : public ConvPoolOpBase<Context> {
|
public:
|
USE_CONV_POOL_BASE_FUNCTIONS(Context);
|
explicit ConvGradientOp(const OperatorDef& operator_def, Workspace* ws)
|
: ConvPoolOpBase<Context>(operator_def, ws),
|
no_bias_(this->template GetSingleArgument<int>("no_bias", 0)) {
|
CAFFE_ENFORCE(
|
!(no_bias_ && OutputSize() == 3),
|
"If bias is not present, you should not have 3 grad output.");
|
CAFFE_ENFORCE(
|
(group_ == 1 || order_ == StorageOrder::NCHW ||
|
std::is_same<Context, CPUContext>::value),
|
"Group convolution only supports NCHW order or CPUContext right now.");
|
}
|
~ConvGradientOp() {}
|
|
bool RunOnDeviceWithOrderNCHW() override;
|
bool RunOnDeviceWithOrderNHWC() override;
|
|
private:
|
Tensor col_buffer_;
|
Tensor bias_multiplier_;
|
Tensor img_shape_device_{Context::GetDeviceType()};
|
Tensor col_buffer_shape_device_{Context::GetDeviceType()};
|
bool no_bias_;
|
// input: X, W, dY
|
// output: dW, db, and optionally dX
|
INPUT_TAGS(INPUT, FILTER, OUTPUT_GRAD);
|
OUTPUT_TAGS(FILTER_GRAD, BIAS_OR_INPUT_GRAD, INPUT_GRAD);
|
};
|
|
} // namespace caffe2
|
|
#endif // CAFFE2_OPERATORS_CONV_OP_H_
|
