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PaddleDetection
提交 5ede6fd4 编写于 作者: X xzl
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delete cuda impl, complete comments, modify variable naming

上级 6b3ae01e
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Showing with 121 addition and 209 deletion
paddle/operators/transpose_op.cc
浏览文件 @ 5ede6fd4
......@@ -13,8 +13,6 @@
limitations under the License. */
#include "paddle/operators/transpose_op.h"
#include <vector>
#include "paddle/framework/ddim.h"
namespace paddle {
namespace operators {
......@@ -27,28 +25,31 @@ class TransposeOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
auto in_dim = ctx.Input<Tensor>("X")->dims();
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Input"),
"Input(Input) should not be null");
auto input_dim = ctx.Input<Tensor>("Input")->dims();
auto axis = ctx.Attr<std::vector<int>>("axis");
size_t in_dim_size = in_dim.size();
size_t input_dim_size = input_dim.size();
size_t axis_size = axis.size();
PADDLE_ENFORCE_EQ(
in_dim_size, axis_size,
"the input tensor dimensions should be equal to the axis size");
PADDLE_ENFORCE_EQ(input_dim_size, axis_size,
"the input tensor's dimension(%d) "
"should be equal to the axis's size(%d)",
input_dim_size, axis_size);
std::vector<int> axis_sorted(axis);
std::sort(axis_sorted.begin(), axis_sorted.end());
for (size_t i = 0; i < axis_sorted.size(); i++) {
PADDLE_ENFORCE_EQ(axis_sorted[i], (int)i,
PADDLE_ENFORCE_EQ(axis_sorted[i], static_cast<int>(i),
"the sorted axis should be [0, 1, ... dims - 1], "
"the dims equals to the input tensor dimensions");
"where the dims is the axis's size");
}
framework::DDim out_dim(in_dim);
framework::DDim output_dim(input_dim);
for (size_t i = 0; i < axis.size(); i++) {
out_dim[i] = in_dim[axis[i]];
output_dim[i] = input_dim[axis[i]];
}
ctx.Output<Tensor>("Out")->Resize(out_dim);
ctx.Output<framework::LoDTensor>("Output")->Resize(output_dim);
}
};
......@@ -57,16 +58,30 @@ class TransposeOpMaker : public framework::OpProtoAndCheckerMaker {
TransposeOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input of transpose op");
AddOutput("Out", "The output of transpose op");
AddInput(
"Input",
"(Tensor)The input tensor, tensors with rank at most 7 are supported");
AddOutput("Output", "(Tensor)The output tensor");
AddAttr<std::vector<int>>(
"axis",
"a list of values, and the size of the list should be "
"(vector<int>)a list of values, and the size of the list should be "
"the same with the input tensor dimensions, the tensor will "
"permute the axes according the the values given");
AddComment(R"DOC(
The Tensor will be permuted according to the axis values given.
For example, given a input tensor of shape(N, C, H, W) and the axis is {0, 2, 3, 1},
The op is very much like the numpy.transpose function in python
For example:
>> input = numpy.arange(6).reshape((2,3))
>> input
array([[0, 1, 2],
[3, 4, 5]])
>> axis = [1, 0]
>> output = input.transpose(axis)
>> output
array([[0, 3],
[1, 4],
[2, 5]])
So, given a input tensor of shape(N, C, H, W) and the axis is {0, 2, 3, 1},
the output tensor shape will be (N, H, W, C)
)DOC");
}
......@@ -78,20 +93,22 @@ class TransposeOpGrad : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
auto out_grad_dims =
ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
auto out_dims = ctx.Input<Tensor>("Out")->dims();
PADDLE_ENFORCE(out_grad_dims == out_dims,
"Out@GRAD dims must equal to Input(X) dims");
x_grad->Resize(x_dims);
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Input"),
"Input(Input) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Output")),
"Input(Output@GRAD) should not be null");
auto input_dims = ctx.Input<Tensor>("Input")->dims();
auto *input_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("Input"));
auto output_grad_dims =
ctx.Input<Tensor>(framework::GradVarName("Output"))->dims();
auto output_dims = ctx.Input<Tensor>("Output")->dims();
PADDLE_ENFORCE(output_grad_dims == output_dims,
"Output@GRAD dims must equal to Input(Input) dims");
input_grad->Resize(input_dims);
}
};
......
paddle/operators/transpose_op.cu
浏览文件 @ 5ede6fd4
......@@ -12,118 +12,11 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/memory/memcpy.h"
#include "paddle/memory/memory.h"
#include "paddle/operators/transpose_op.h"
namespace paddle {
namespace operators {
template <typename T>
__global__ void transpose_kernel(int nthreads, const T* in_data, T* out_data,
int* offset_buffer, int ndims) {
int* in_offset = offset_buffer;
int* out_offset = offset_buffer + ndims;
int* axis = offset_buffer + ndims * 2;
int to_index = blockIdx.x * blockDim.x + threadIdx.x;
if (to_index < nthreads) {
int from_index = 0;
int temp = to_index;
for (size_t i = 0; i < ndims; i++) {
from_index += (temp / out_offset[i]) * in_offset[axis[i]];
temp = temp % out_offset[i];
}
out_data[to_index] = in_data[from_index];
}
}
template <typename T>
void TransposeCUDA(const framework::ExecutionContext& context,
const framework::Tensor& in, framework::Tensor& out,
std::vector<int> axis) {
auto* in_data = in.template data<T>();
auto* out_data = out.template mutable_data<T>(context.GetPlace());
auto in_dim = in.dims();
auto out_dim = out.dims();
auto data_size = product(in_dim);
size_t ndims = in_dim.size();
std::vector<int> in_offset(ndims, 1);
std::vector<int> out_offset(ndims, 1);
auto cpu_place = platform::CPUPlace();
auto gpu_place = boost::get<platform::GPUPlace>(context.GetPlace());
// Get a host_buffer to cache the input offset, output offset and the axis.
std::vector<int64_t> buffer_dim_shape(1, ndims * 3);
auto buffer_dims = framework::make_ddim(buffer_dim_shape);
framework::Tensor host_buffer;
int* host_buffer_data = host_buffer.mutable_data<int>(buffer_dims, cpu_place);
for (int i = ndims - 2; i >= 0; i--) {
in_offset[i] = in_offset[i + 1] * in_dim[i + 1];
out_offset[i] = out_offset[i + 1] * out_dim[i + 1];
}
// copy the data to the host_buffer
for (int i = 0; i < ndims; i++) {
host_buffer_data[i] = in_offset[i];
host_buffer_data[i + ndims] = out_offset[i];
host_buffer_data[i + ndims * 2] = axis[i];
}
// Get a device_buffer to cache the input offset, output offset and the axis.
auto offset_buffer = memory::Alloc(gpu_place, ndims * 3 * sizeof(int));
auto* cuda_device_context = reinterpret_cast<platform::CUDADeviceContext*>(
const_cast<platform::DeviceContext*>(context.device_context_));
// copy the host_buffer data to the device_buffer
memory::Copy(gpu_place, offset_buffer, cpu_place, host_buffer_data,
ndims * 3 * sizeof(int), cuda_device_context->stream());
int block = 512;
int grid = (data_size + block - 1) / block;
transpose_kernel<T><<<grid, block>>>(data_size, in_data, out_data,
static_cast<int*>(offset_buffer), ndims);
memory::Free(gpu_place, offset_buffer);
}
template <typename T>
class TransposeCUDAKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
PADDLE_ENFORCE(platform::is_gpu_place(context.GetPlace()),
"It must use GPUPlace.");
auto* in = context.Input<framework::Tensor>("X");
auto* out = context.Output<framework::Tensor>("Out");
auto axis = context.Attr<std::vector<int>>("axis");
TransposeCUDA<T>(context, *in, *out, axis);
}
};
template <typename T>
class TransposeGradCUDAKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
PADDLE_ENFORCE(platform::is_gpu_place(context.GetPlace()),
"It must use GPUPlace.");
auto* in = context.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* out = context.Output<framework::Tensor>(framework::GradVarName("X"));
auto axis_temp = context.Attr<std::vector<int>>("axis");
std::vector<int> axis(axis_temp);
for (size_t i = 0; i < axis.size(); i++) {
axis[axis_temp[i]] = i;
}
TransposeCUDA<T>(context, *in, *out, axis);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(transpose, ops::TransposeCUDAKernel<float>);
REGISTER_OP_GPU_KERNEL(transpose_grad, ops::TransposeGradCUDAKernel<float>);
REGISTER_OP_GPU_KERNEL(transpose,
ops::TransposeKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
transpose_grad,
ops::TransposeGradKernel<paddle::platform::GPUPlace, float>);
paddle/operators/transpose_op.h
浏览文件 @ 5ede6fd4
......@@ -20,39 +20,8 @@
namespace paddle {
namespace operators {
template <typename Place, typename T>
void NaiveCpuTranspose(const framework::ExecutionContext& context,
const framework::Tensor& in, framework::Tensor& out,
std::vector<int> axis) {
auto in_data = in.data<T>();
auto out_data = out.mutable_data<T>(context.GetPlace());
auto in_dim = in.dims();
auto out_dim = out.dims();
size_t ndims = in_dim.size();
std::vector<int> in_offset(ndims, 1);
std::vector<int> out_offset(ndims, 1);
for (int i = ndims - 2; i >= 0; i--) {
in_offset[i] = in_offset[i + 1] * in_dim[i + 1];
out_offset[i] = out_offset[i + 1] * out_dim[i + 1];
}
size_t data_size = product(in_dim);
for (size_t to_index = 0; to_index < data_size; to_index++) {
int from_index = 0;
int temp = to_index;
for (size_t i = 0; i < ndims; i++) {
from_index += (temp / out_offset[i]) * in_offset[axis[i]];
temp = temp % out_offset[i];
}
out_data[to_index] = in_data[from_index];
}
}
template <typename Place, typename T, int Dims>
void DoTranspose(const framework::ExecutionContext& context,
void EigenTranspose(const framework::ExecutionContext& context,
const framework::Tensor& in, framework::Tensor& out,
std::vector<int> axis) {
Eigen::array<int, Dims> permute;
......@@ -72,28 +41,32 @@ template <typename Place, typename T>
class TransposeKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in = context.Input<framework::Tensor>("X");
auto* out = context.Output<framework::Tensor>("Out");
out->mutable_data<T>(context.GetPlace());
auto* input = context.Input<framework::Tensor>("Input");
auto* output = context.Output<framework::Tensor>("Output");
output->mutable_data<T>(context.GetPlace());
auto axis = context.Attr<std::vector<int>>("axis");
int ndims = axis.size();
switch (ndims) {
case 1:
break;
case 2:
DoTranspose<Place, T, 2>(context, *in, *out, axis);
EigenTranspose<Place, T, 2>(context, *input, *output, axis);
break;
case 3:
DoTranspose<Place, T, 3>(context, *in, *out, axis);
EigenTranspose<Place, T, 3>(context, *input, *output, axis);
break;
case 4:
DoTranspose<Place, T, 4>(context, *in, *out, axis);
EigenTranspose<Place, T, 4>(context, *input, *output, axis);
break;
case 5:
DoTranspose<Place, T, 5>(context, *in, *out, axis);
EigenTranspose<Place, T, 5>(context, *input, *output, axis);
break;
default:
NaiveCpuTranspose<Place, T>(context, *in, *out, axis);
case 6:
EigenTranspose<Place, T, 6>(context, *input, *output, axis);
break;
default:
PADDLE_THROW("Tensors with rank at most 6 are supported");
}
}
};
......@@ -102,9 +75,11 @@ template <typename Place, typename T>
class TransposeGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in = context.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* out = context.Output<framework::Tensor>(framework::GradVarName("X"));
out->mutable_data<T>(context.GetPlace());
auto* output_grad =
context.Input<framework::Tensor>(framework::GradVarName("Output"));
auto* input_grad =
context.Output<framework::Tensor>(framework::GradVarName("Input"));
input_grad->mutable_data<T>(context.GetPlace());
auto axis_temp = context.Attr<std::vector<int>>("axis");
std::vector<int> axis(axis_temp);
......@@ -116,21 +91,25 @@ class TransposeGradKernel : public framework::OpKernel {
int ndims = axis.size();
switch (ndims) {
case 1:
break;
case 2:
DoTranspose<Place, T, 2>(context, *in, *out, axis);
EigenTranspose<Place, T, 2>(context, *output_grad, *input_grad, axis);
break;
case 3:
DoTranspose<Place, T, 3>(context, *in, *out, axis);
EigenTranspose<Place, T, 3>(context, *output_grad, *input_grad, axis);
break;
case 4:
DoTranspose<Place, T, 4>(context, *in, *out, axis);
EigenTranspose<Place, T, 4>(context, *output_grad, *input_grad, axis);
break;
case 5:
DoTranspose<Place, T, 5>(context, *in, *out, axis);
EigenTranspose<Place, T, 5>(context, *output_grad, *input_grad, axis);
break;
default:
NaiveCpuTranspose<Place, T>(context, *in, *out, axis);
case 6:
EigenTranspose<Place, T, 6>(context, *output_grad, *input_grad, axis);
break;
default:
PADDLE_THROW("Tensors with rank at most 6 are supported");
}
}
};
......
python/paddle/v2/framework/tests/test_transpose_op.py
浏览文件 @ 5ede6fd4
import unittest
import numpy as np
from gradient_checker import GradientChecker
from op_test_util import OpTestMeta
from paddle.v2.framework.op import Operator
from op_test import OpTest
class TestTransposeOp(unittest.TestCase):
__metaclass__ = OpTestMeta
class TestTransposeOp(OpTest):
def setUp(self):
self.type = "transpose"
self.inputs = {'X': np.random.random((3, 4)).astype("float32"), }
self.attrs = {'axis': [1, 0]}
self.outputs = {'Out': self.inputs['X'].transpose((1, 0))}
self.initTestCase()
self.op_type = "transpose"
self.inputs = {'Input': np.random.random(self.shape).astype("float32")}
self.attrs = {'axis': list(self.axis)}
self.outputs = {'Output': self.inputs['Input'].transpose(self.axis)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['Input'], 'Output')
def initTestCase(self):
self.shape = (3, 4)
self.axis = (1, 0)
class TestCase1(TestTransposeOp):
def initTestCase(self):
self.shape = (3, 4, 5)
self.axis = (0, 2, 1)
class TestCase2(TestTransposeOp):
def initTestCase(self):
self.shape = (2, 3, 4, 5)
self.axis = (0, 2, 3, 1)
class TestCase3(TestTransposeOp):
def initTestCase(self):
self.shape = (2, 3, 4, 5, 6)
self.axis = (4, 2, 3, 1, 0)
class TransposeGradOpTest(GradientChecker):
def test_transpose(self):
op = Operator("transpose", X="X", Out="Out", axis=[1, 0])
inputs = {'X': np.random.random((32, 84)).astype("float32"), }
self.check_grad(op, inputs, set(["X"]), "Out", max_relative_error=0.5)
class TestCase4(TestTransposeOp):
def initTestCase(self):
self.shape = (2, 3, 4, 5, 6, 1)
self.axis = (4, 2, 3, 1, 0, 5)
if __name__ == '__main__':
......
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