m53297601
/
Paddle
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

speed up box coder in CPU, test=develop

Browse Source
revert-15661-fix-cpu-broadcast
jerrywgz 6 years ago
parent 0766d404ba
commit ceb412b0ae
5 changed files with 60 additions and 88 deletions
Show Stats Download Patch File Download Diff File

20
paddle/fluid/operators/detection/box_coder_op.cc
Unescape View File

@ -38,20 +38,12 @@ class BoxCoderOp : public framework::OperatorWithKernel {
"The shape of PriorBox is [N, 4]");
if (ctx->HasInput("PriorBoxVar")) {
auto prior_box_var_dims = ctx->GetInputDim("PriorBoxVar");
PADDLE_ENFORCE(
prior_box_var_dims.size() == 1 || prior_box_var_dims.size() == 2,
"Input(PriorBoxVar) of BoxCoderOp should be 1 or 2.");
if (prior_box_var_dims.size() == 1) {
PADDLE_ENFORCE_EQ(
prior_box_var_dims[0], 4,
"The 1st dimension of Input(PriorBoxVar) should be 4"
"when the rank is 1.");
} else {
PADDLE_ENFORCE_EQ(
prior_box_dims, prior_box_var_dims,
"The dimension of Input(PriorBoxVar) should be equal to"
"the dimension of Input(PriorBox when the rank is 2.)");
}
PADDLE_ENFORCE(prior_box_var_dims.size() == 2,
"Input(PriorBoxVar) of BoxCoderOp should be 2.");
PADDLE_ENFORCE_EQ(
prior_box_dims, prior_box_var_dims,
"The dimension of Input(PriorBoxVar) should be equal to"
"the dimension of Input(PriorBox) when the rank is 2.");
}
}

10
paddle/fluid/operators/detection/box_coder_op.cu
Unescape View File

@ -56,10 +56,7 @@ __global__ void EncodeCenterSizeKernel(
output[idx * len + 2] = log(fabs(target_box_width / prior_box_width));
output[idx * len + 3] = log(fabs(target_box_height / prior_box_height));
if (prior_box_var_data) {
int prior_var_offset = 0;
if (prior_box_var_size == 2) {
prior_var_offset = col_idx * len;
}
int prior_var_offset = col_idx * len;
output[idx * len] /= prior_box_var_data[prior_var_offset];
output[idx * len + 1] /= prior_box_var_data[prior_var_offset + 1];
output[idx * len + 2] /= prior_box_var_data[prior_var_offset + 2];
@ -99,10 +96,7 @@ __global__ void DecodeCenterSizeKernel(
T box_var_x = T(1), box_var_y = T(1);
T box_var_w = T(1), box_var_h = T(1);
if (prior_box_var_data) {
int prior_var_offset = 0;
if (prior_box_var_size == 2) {
prior_var_offset = axis == 0 ? col_idx * len : row_idx * len;
}
int prior_var_offset = axis == 0 ? col_idx * len : row_idx * len;
box_var_x = prior_box_var_data[prior_var_offset];
box_var_y = prior_box_var_data[prior_var_offset + 1];
box_var_w = prior_box_var_data[prior_var_offset + 2];

77
paddle/fluid/operators/detection/box_coder_op.h
Unescape View File

@ -79,10 +79,7 @@ class BoxCoderKernel : public framework::OpKernel<T> {
output[offset + 3] =
std::log(std::fabs(target_box_height / prior_box_height));
if (prior_box_var) {
int prior_var_offset = 0;
if (prior_box_var->dims().size() == 2) {
prior_var_offset = j * len;
}
int prior_var_offset = j * len;
output[offset] /= prior_box_var_data[prior_var_offset];
output[offset + 1] /= prior_box_var_data[prior_var_offset + 1];
output[offset + 2] /= prior_box_var_data[prior_var_offset + 2];
@ -95,11 +92,12 @@ class BoxCoderKernel : public framework::OpKernel<T> {
}
}
}
template <int axis, int var_size>
void DecodeCenterSize(const framework::Tensor* target_box,
const framework::Tensor* prior_box,
const framework::Tensor* prior_box_var,
const bool normalized, const int axis,
const std::vector<float> variance, T* output) const {
const bool normalized, std::vector<float> variance,
T* output) const {
int64_t row = target_box->dims()[0];
int64_t col = target_box->dims()[1];
int64_t len = target_box->dims()[2];
@ -107,19 +105,17 @@ class BoxCoderKernel : public framework::OpKernel<T> {
auto* target_box_data = target_box->data<T>();
auto* prior_box_data = prior_box->data<T>();
const T* prior_box_var_data = nullptr;
if (prior_box_var) prior_box_var_data = prior_box_var->data<T>();
if (var_size == 2) prior_box_var_data = prior_box_var->data<T>();
int prior_box_offset = 0;
T var_data[4] = {1., 1., 1., 1.};
T* var_ptr = var_data;
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for collapse(2)
#endif
for (int64_t i = 0; i < row; ++i) {
for (int64_t j = 0; j < col; ++j) {
size_t offset = i * col * len + j * len;
if (axis == 0) {
prior_box_offset = j * len;
} else if (axis == 1) {
prior_box_offset = i * len;
}
prior_box_offset = axis == 0 ? j * len : i * len;
T prior_box_width = prior_box_data[prior_box_offset + 2] -
prior_box_data[prior_box_offset] +
(normalized == false);
@ -133,26 +129,18 @@ class BoxCoderKernel : public framework::OpKernel<T> {
T target_box_center_x = 0, target_box_center_y = 0;
T target_box_width = 0, target_box_height = 0;
T box_var_x = T(1), box_var_y = T(1);
T box_var_w = T(1), box_var_h = T(1);
if (prior_box_var) {
int prior_var_offset = 0;
if (prior_box_var->dims().size() == 2) {
if (axis == 0)
prior_var_offset = j * len;
else if (axis == 1)
prior_var_offset = i * len;
}
box_var_x = prior_box_var_data[prior_var_offset];
box_var_y = prior_box_var_data[prior_var_offset + 1];
box_var_w = prior_box_var_data[prior_var_offset + 2];
box_var_h = prior_box_var_data[prior_var_offset + 3];
} else if (!(variance.empty())) {
box_var_x = static_cast<T>(variance[0]);
box_var_y = static_cast<T>(variance[1]);
box_var_w = static_cast<T>(variance[2]);
box_var_h = static_cast<T>(variance[3]);
int prior_var_offset = axis == 0 ? j * len : i * len;
if (var_size == 2) {
std::memcpy(var_ptr, prior_box_var_data + prior_var_offset,
4 * sizeof(T));
} else if (var_size == 1) {
var_ptr = reinterpret_cast<T*>(variance.data());
}
T box_var_x = *var_ptr;
T box_var_y = *(var_ptr + 1);
T box_var_w = *(var_ptr + 2);
T box_var_h = *(var_ptr + 3);
target_box_center_x =
box_var_x * target_box_data[offset] * prior_box_width +
prior_box_center_x;
@ -211,8 +199,31 @@ class BoxCoderKernel : public framework::OpKernel<T> {
EncodeCenterSize(target_box, prior_box, prior_box_var, normalized,
variance, output);
} else if (code_type == BoxCodeType::kDecodeCenterSize) {
DecodeCenterSize(target_box, prior_box, prior_box_var, normalized, axis,
variance, output);
if (prior_box_var) {
if (axis == 0) {
DecodeCenterSize<0, 2>(target_box, prior_box, prior_box_var,
normalized, variance, output);
} else {
DecodeCenterSize<1, 2>(target_box, prior_box, prior_box_var,
normalized, variance, output);
}
} else if (!(variance.empty())) {
if (axis == 0) {
DecodeCenterSize<0, 1>(target_box, prior_box, prior_box_var,
normalized, variance, output);
} else {
DecodeCenterSize<1, 1>(target_box, prior_box, prior_box_var,
normalized, variance, output);
}
} else {
if (axis == 0) {
DecodeCenterSize<0, 0>(target_box, prior_box, prior_box_var,
normalized, variance, output);
} else {
DecodeCenterSize<1, 0>(target_box, prior_box, prior_box_var,
normalized, variance, output);
}
}
}
}
};

8
python/paddle/fluid/layers/detection.py
Unescape View File

@ -397,10 +397,10 @@ def box_coder(prior_box,
input is image feature map, they are close to
the origin of the coordinate system. [xmax, ymax]
is the right bottom coordinate of the anchor box.
prior_box_var(Variable|list): prior_box_var supports two types of input.
One is variable with shape [M, 4] holds M group.
The other one is list consist of 4 elements
shared by all boxes.
prior_box_var(Variable|list|None): prior_box_var supports two types
of input. One is variable with shape [M, 4]
holds M group. The other one is list consist of
4 elements shared by all boxes.
target_box(Variable): This input can be a 2-D LoDTensor with shape
[N, 4] when code_type is 'encode_center_size'.
This input also can be a 3-D Tensor with shape

33
python/paddle/fluid/tests/unittests/test_box_coder_op.py
Unescape View File

@ -34,7 +34,9 @@ def box_decoder(t_box, p_box, pb_v, output_box, norm, axis=0):
pb_y = pb_y.reshape(shape)
if pb_v.ndim == 2:
pb_v = pb_v.reshape(1, pb_v.shape[0], pb_v.shape[1])
var_shape = (1, pb_v.shape[0], pb_v.shape[1]) if axis == 0 else (
pb_v.shape[0], 1, pb_v.shape[1])
pb_v = pb_v.reshape(var_shape)
if pb_v.ndim == 1:
tb_x = pb_v[0] * t_box[:, :, 0] * pb_w + pb_x
tb_y = pb_v[1] * t_box[:, :, 1] * pb_h + pb_y
@ -125,33 +127,6 @@ class TestBoxCoderOp(OpTest):
self.outputs = {'OutputBox': output_box}
class TestBoxCoderOpWithOneRankVar(OpTest):
def test_check_output(self):
self.check_output()
def setUp(self):
self.op_type = "box_coder"
lod = [[1, 1, 1, 1, 1]]
prior_box = np.random.random((81, 4)).astype('float32')
prior_box_var = np.random.random((4)).astype('float32')
target_box = np.random.random((20, 81, 4)).astype('float32')
code_type = "DecodeCenterSize"
box_normalized = False
output_box = batch_box_coder(prior_box, prior_box_var, target_box,
lod[0], code_type, box_normalized)
self.inputs = {
'PriorBox': prior_box,
'PriorBoxVar': prior_box_var,
'TargetBox': target_box,
}
self.attrs = {
'code_type': 'decode_center_size',
'box_normalized': False
}
self.outputs = {'OutputBox': output_box}
class TestBoxCoderOpWithoutBoxVar(OpTest):
def test_check_output(self):
self.check_output()
@ -210,7 +185,7 @@ class TestBoxCoderOpWithAxis(OpTest):
self.op_type = "box_coder"
lod = [[1, 1, 1, 1, 1]]
prior_box = np.random.random((30, 4)).astype('float32')
prior_box_var = np.random.random((4)).astype('float32')
prior_box_var = np.random.random((30, 4)).astype('float32')
target_box = np.random.random((30, 81, 4)).astype('float32')
code_type = "DecodeCenterSize"
box_normalized = False

Write Preview
Loading…
Cancel
Save