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

Enhance the AvgPooling to support optional exclude-mode

Browse Source
release/0.11.0
guosheng 7 years ago
parent 1b6dcc2fe8
commit fe6af6b6ac
13 changed files with 107 additions and 37 deletions
Show Stats Download Patch File Download Diff File

6
paddle/cuda/include/stub/hl_cnn_stub.h
Unescape View File

@ -68,7 +68,8 @@ inline void hl_avgpool_forward(const int frameCnt,
const int paddingH,
const int paddingW,
real* tgtData,
const int tgtStride) {}
const int tgtStride,
const bool excludeMode) {}
inline void hl_avgpool_backward(const int frameCnt,
const real* outGrad,
@ -86,7 +87,8 @@ inline void hl_avgpool_backward(const int frameCnt,
real scaleA,
real scaleB,
real* backGrad,
const int outStride) {}
const int outStride,
const bool excludeMode) {}
inline void hl_maxpool3D_forward(const int frameCnt,
const real* inputData,

28
paddle/cuda/src/hl_cuda_cnn.cu
Unescape View File

@ -210,7 +210,8 @@ __global__ void KeAvgPoolForward(const int nthreads,
const int padH,
const int padW,
real* tgtData,
const int tgtStride) {
const int tgtStride,
const bool excludeMode) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < nthreads) {
int pw = index % pooledW;
@ -224,7 +225,8 @@ __global__ void KeAvgPoolForward(const int nthreads,
int wend = min(wstart + sizeX, width);
hstart = max(hstart, 0);
wstart = max(wstart, 0);
int pool_size = (hend - hstart) * (wend - wstart);
int poolSize =
excludeMode ? (hend - hstart) * (wend - wstart) : sizeY * sizeX;
real aveval = 0;
inputData += (frameNum * channels + c) * height * width;
@ -235,7 +237,7 @@ __global__ void KeAvgPoolForward(const int nthreads,
}
int tgtIndex =
index % (pooledW * pooledH * channels) + frameNum * tgtStride;
tgtData[tgtIndex] = aveval / pool_size;
tgtData[tgtIndex] = aveval / poolSize;
}
}
@ -253,7 +255,8 @@ void hl_avgpool_forward(const int frameCnt,
const int paddingH,
const int paddingW,
real* tgtData,
const int tgtStride) {
const int tgtStride,
const bool excludeMode) {
int num_kernels = pooledH * pooledW * channels * frameCnt;
int blocks = (num_kernels + 1024 - 1) / 1024;
KeAvgPoolForward<<<blocks, 1024, 0, STREAM_DEFAULT>>>(num_kernels,
@ -270,7 +273,8 @@ void hl_avgpool_forward(const int frameCnt,
paddingH,
paddingW,
tgtData,
tgtStride);
tgtStride,
excludeMode);
CHECK_SYNC("hl_avgpool_forward failed");
}
@ -290,7 +294,8 @@ __global__ void KeAvgPoolBackward(const int nthreads,
real scaleA,
real scaleB,
real* tgtGrad,
const int outStride) {
const int outStride,
const bool excludeMode) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < nthreads) {
int offsetW = index % width + padW;
@ -314,8 +319,9 @@ __global__ void KeAvgPoolBackward(const int nthreads,
int wstart = pw * strideW - padW;
int wend = min(wstart + sizeX, width);
wstart = max(wstart, 0);
int poolsize = (hend - hstart) * (wend - wstart);
gradient += outGrad[ph * pooledW + pw] / poolsize;
int poolSize =
excludeMode ? (hend - hstart) * (wend - wstart) : sizeY * sizeX;
gradient += outGrad[ph * pooledW + pw] / poolSize;
}
}
tgtGrad[index] = scaleB * tgtGrad[index] + scaleA * gradient;
@ -338,7 +344,8 @@ void hl_avgpool_backward(const int frameCnt,
real scaleA,
real scaleB,
real* backGrad,
const int outStride) {
const int outStride,
const bool excludeMode) {
int num_kernels = height * width * channels * frameCnt;
int blocks = (num_kernels + 1024 - 1) / 1024;
@ -358,7 +365,8 @@ void hl_avgpool_backward(const int frameCnt,
scaleA,
scaleB,
backGrad,
outStride);
outStride,
excludeMode);
CHECK_SYNC("hl_avgpool_backward failed");
}

2
paddle/gserver/layers/PoolLayer.cpp
Unescape View File

@ -45,6 +45,8 @@ bool PoolLayer::init(const LayerMap& layerMap,
strideY_ = conf.has_stride_y() ? conf.stride_y() : conf.stride();
confPaddingY_ = conf.has_padding_y() ? conf.padding_y() : conf.padding();
outputY_ = conf.has_output_y() ? conf.output_y() : conf.output_x();
excludeMode_ = conf.has_exclude_mode() ? conf.exclude_mode() : true;
return true;
}

2
paddle/gserver/layers/PoolLayer.h
Unescape View File

@ -38,6 +38,8 @@ protected:
std::string poolType_;
bool excludeMode_;
public:
explicit PoolLayer(const LayerConfig& config) : Layer(config) {}

8
paddle/gserver/layers/PoolProjection.cpp
Unescape View File

@ -36,6 +36,8 @@ PoolProjection::PoolProjection(const ProjectionConfig& config,
strideY_ = conf.has_stride_y() ? conf.stride_y() : conf.stride();
confPaddingY_ = conf.has_padding_y() ? conf.padding_y() : conf.padding();
outputY_ = conf.has_output_y() ? conf.output_y() : conf.output_x();
excludeMode_ = conf.has_exclude_mode() ? conf.exclude_mode() : true;
}
size_t PoolProjection::getSize() {
@ -141,7 +143,8 @@ void AvgPoolProjection::forward() {
outputY_,
outputX_,
confPaddingY_,
confPadding_);
confPadding_,
excludeMode_);
}
void AvgPoolProjection::backward(const UpdateCallback& callback) {
@ -166,6 +169,7 @@ void AvgPoolProjection::backward(const UpdateCallback& callback) {
1,
1,
confPaddingY_,
confPadding_);
confPadding_,
excludeMode_);
}
} // namespace paddle

1
paddle/gserver/layers/PoolProjection.h
Unescape View File

@ -28,6 +28,7 @@ protected:
int confPaddingY_, confPadding_;
size_t channels_;
std::string poolType_;
bool excludeMode_;
public:
PoolProjection(const ProjectionConfig& config,

16
paddle/gserver/tests/test_LayerGrad.cpp
Unescape View File

@ -1211,7 +1211,10 @@ void setPoolConfig(TestConfig* config,
pool->set_output_y(oh);
}
void testPoolLayer(const string& poolType, bool trans, bool useGpu) {
void testPoolLayer(const string& poolType,
bool trans,
bool useGpu,
bool excludeMode = true) {
TestConfig config;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 3136, 0});
LayerInputConfig* input = config.layerConfig.add_inputs();
@ -1219,6 +1222,7 @@ void testPoolLayer(const string& poolType, bool trans, bool useGpu) {
pool->set_img_size(14);
pool->set_img_size_y(14);
pool->set_exclude_mode(excludeMode);
setPoolConfig(&config, pool, poolType);
config.layerConfig.set_size(pool->output_x() * pool->output_y() *
pool->channels());
@ -1250,16 +1254,26 @@ void testPoolLayer2(const string& poolType, bool trans, bool useGpu) {
TEST(Layer, PoolLayer) {
testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ false);
testPoolLayer("avg-projection",
/* trans= */ false,
/* useGpu= */ false,
/* excludeMode= */ false);
testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ false);
testPoolLayer("max-pool-with-mask", /* trans= */ false, /* useGpu= */ false);
#ifdef PADDLE_WITH_CUDA
testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ true);
testPoolLayer("avg-projection",
/* trans= */ false,
/* useGpu= */ true,
/* excludeMode= */ false);
testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ true);
testPoolLayer("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true);
testPoolLayer("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true);
testPoolLayer2("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true);
testPoolLayer2("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true);
testPoolLayer2(
"cudnn-avg-incl-pad-pool", /* trans= */ false, /* useGpu= */ true);
testPoolLayer("max-pool-with-mask", /* trans= */ false, /* useGpu= */ true);
#endif
}

24
paddle/math/Matrix.cpp
Unescape View File

@ -1130,7 +1130,8 @@ void GpuMatrix::avgPoolForward(Matrix& inputMat,
size_t outputH,
size_t outputW,
size_t paddingH,
size_t paddingW) {
size_t paddingW,
bool excludeMode) {
CHECK(inputMat.useGpu_ == true) << "Matrix type are not equal";
real* inputData = inputMat.getData();
@ -1153,7 +1154,8 @@ void GpuMatrix::avgPoolForward(Matrix& inputMat,
paddingH,
paddingW,
data_,
getStride());
getStride(),
excludeMode);
}
void GpuMatrix::avgPoolBackward(Matrix& outGrad,
@ -1168,7 +1170,8 @@ void GpuMatrix::avgPoolBackward(Matrix& outGrad,
real scaleTargets,
real scaleOutput,
size_t paddingH,
size_t paddingW) {
size_t paddingW,
bool excludeMode) {
CHECK(outGrad.useGpu_ == true) << "Matrix type are not equal";
real* outDiff = outGrad.getData();
@ -1194,7 +1197,8 @@ void GpuMatrix::avgPoolBackward(Matrix& outGrad,
scaleTargets,
scaleOutput,
data_,
outGrad.getStride());
outGrad.getStride(),
excludeMode);
}
void GpuMatrix::maxPool3DForward(Matrix& inputMat,
@ -2136,7 +2140,8 @@ void CpuMatrix::avgPoolForward(Matrix& input,
size_t outputH,
size_t outputW,
size_t paddingH,
size_t paddingW) {
size_t paddingW,
bool excludeMode) {
// The main loop
size_t num = input.getHeight();
size_t inLength = imgSizeH * imgSizeW;
@ -2165,7 +2170,8 @@ void CpuMatrix::avgPoolForward(Matrix& input,
tgtData[ph * outputW + pw] += inData[h * imgSizeW + w];
}
}
int poolSize = (hend - hstart) * (wend - wstart);
int poolSize =
excludeMode ? (hend - hstart) * (wend - wstart) : sizeY * sizeX;
CHECK(poolSize);
tgtData[ph * outputW + pw] /= poolSize;
}
@ -2189,7 +2195,8 @@ void CpuMatrix::avgPoolBackward(Matrix& input,
real scaleTargets,
real scaleOutput,
size_t paddingH,
size_t paddingW) {
size_t paddingW,
bool excludeMode) {
size_t num = input.getHeight();
size_t channels = input.getWidth() / outputH / outputW;
size_t inLength = imgSizeH * imgSizeW;
@ -2211,7 +2218,8 @@ void CpuMatrix::avgPoolBackward(Matrix& input,
int wstart = pw * strideW - paddingW;
int wend = std::min(wstart + sizeX, imgSizeW);
wstart = std::max(wstart, 0);
int poolSize = (hend - hstart) * (wend - wstart);
int poolSize =
excludeMode ? (hend - hstart) * (wend - wstart) : sizeY * sizeX;
CHECK(poolSize);
for (int h = hstart; h < hend; ++h) {

19
paddle/math/Matrix.h
Unescape View File

@ -911,7 +911,8 @@ public:
size_t outputH,
size_t outputW,
size_t paddingH,
size_t paddingW) {
size_t paddingW,
bool excludeMode = true) {
LOG(FATAL) << "Not implemeted";
}
@ -927,9 +928,11 @@ public:
real scaleTargets,
real scaleOutput,
size_t paddingH,
size_t paddingW) {
size_t paddingW,
bool excludeMode = true) {
LOG(FATAL) << "Not implemeted";
}
/**
* Pooling 3D forward operation, pick out the largest element
* in the sizeX of value
@ -1458,7 +1461,8 @@ public:
size_t outputH,
size_t outputW,
size_t paddingH,
size_t paddingW);
size_t paddingW,
bool excludeMode = true);
void avgPoolBackward(Matrix& input,
size_t imgSizeH,
@ -1472,7 +1476,8 @@ public:
real scaleTargets,
real scaleOutput,
size_t paddingH,
size_t paddingW);
size_t paddingW,
bool excludeMode = true);
void maxPool3DForward(Matrix& inputMat,
Matrix& maxPoolIdx,
@ -1730,7 +1735,8 @@ public:
size_t outputH,
size_t outputW,
size_t paddingH,
size_t paddingW);
size_t paddingW,
bool excludeMode = true);
void avgPoolBackward(Matrix& input,
size_t imgSizeH,
@ -1744,7 +1750,8 @@ public:
real scaleTargets,
real scaleOutput,
size_t paddingH,
size_t paddingW);
size_t paddingW,
bool excludeMode = true);
void maxPool3DForward(Matrix& inputMat,
Matrix& maxPoolIdx,

2
proto/ModelConfig.proto
Unescape View File

@ -139,6 +139,8 @@ message PoolConfig {
optional uint32 output_z = 16 [ default = 1 ];
optional uint32 img_size_z = 17 [ default = 1 ];
optional uint32 padding_z = 18 [ default = 1 ];
optional bool exclude_mode = 19 [ default = true ];
}
message SppConfig {

9
python/paddle/trainer/config_parser.py
Unescape View File

@ -1233,7 +1233,7 @@ def parse_bilinear(bilinear, input_layer_name, bilinear_conf):
bilinear_conf.out_size_y = bilinear.out_size_y
def parse_pool(pool, input_layer_name, pool_conf, ceil_mode):
def parse_pool(pool, input_layer_name, pool_conf, ceil_mode, exclude_mode):
pool_conf.pool_type = pool.pool_type
config_assert(pool.pool_type in [
'max-projection', 'avg-projection', 'max-pool-with-mask', 'cudnn-max-pool', 'cudnn-avg-pool'
@ -1263,6 +1263,8 @@ def parse_pool(pool, input_layer_name, pool_conf, ceil_mode):
pool_conf.padding_y,
pool_conf.stride_y, not ceil_mode)
pool_conf.exclude_mode = exclude_mode
def parse_pool3d(pool, input_layer_name, pool_conf, ceil_mode):
pool_conf.pool_type = pool.pool_type
@ -2303,7 +2305,8 @@ class NormLayer(LayerBase):
class PoolLayer(LayerBase):
layer_type = 'pool'
def __init__(self, name, inputs, ceil_mode=True, **xargs):
def __init__(self, name, inputs, ceil_mode=True, exclude_mode=True,
**xargs):
use_mkldnn = int(g_command_config_args.get("use_mkldnn", 0))
if self.layer_type == "mkldnn_pool":
config_assert(use_mkldnn, "mkldnn_pool only support MKLDNN")
@ -2314,7 +2317,7 @@ class PoolLayer(LayerBase):
input_layer = self.get_input_layer(input_index)
pool_conf = self.config.inputs[input_index].pool_conf
parse_pool(self.inputs[input_index].pool, input_layer.name,
pool_conf, ceil_mode)
pool_conf, ceil_mode, exclude_mode)
self.set_cnn_layer(name, pool_conf.output_y, pool_conf.output_x,
pool_conf.channels)

14
python/paddle/trainer_config_helpers/layers.py
Unescape View File

@ -21,7 +21,7 @@ from .activations import LinearActivation, SigmoidActivation, TanhActivation, \
ReluActivation, IdentityActivation, SoftmaxActivation, BaseActivation
from .evaluators import *
from .poolings import MaxPooling, AvgPooling, MaxWithMaskPooling, BasePoolingType, \
CudnnAvgPooling, CudnnMaxPooling
CudnnAvgPooling, CudnnAvgInclPadPooling, CudnnMaxPooling
from .attrs import *
from .default_decorators import *
@ -2709,7 +2709,8 @@ def img_pool_layer(input,
pool_size_y=None,
stride_y=None,
padding_y=None,
ceil_mode=True):
ceil_mode=True,
exclude_mode=True):
"""
Image pooling Layer.
@ -2773,10 +2774,14 @@ def img_pool_layer(input,
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:param ceil_mode: Wether to use the ceil function to calculate output height and width.
:param ceil_mode: Whether to use the ceil function to calculate output height and width.
True is the default. If it is set to False, the floor function will
be used.
:type ceil_mode: bool
:param exclude_mode: Whether to exclude the padding cells when calculating, but only
work when pool_type is AvgPooling. If use cudnn, use CudnnAvgPooling
or CudnnAvgInclPadPooling as pool_type to identify.
:type exclude_mode: bool
:return: LayerOutput object.
:rtype: LayerOutput
"""
@ -2790,7 +2795,7 @@ def img_pool_layer(input,
pool_type.name = 'avg'
assert type(pool_type) in [AvgPooling, MaxPooling, MaxWithMaskPooling, CudnnAvgPooling,
CudnnMaxPooling], \
CudnnMaxPooling, CudnnAvgInclPadPooling], \
"only (Cudnn)AvgPooling, (Cudnn)MaxPooling, MaxWithMaskPooling are supported"
type_name = pool_type.name + '-projection' \
@ -2819,6 +2824,7 @@ def img_pool_layer(input,
padding_y=padding_y))
],
ceil_mode=ceil_mode,
exclude_mode=exclude_mode,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,

13
python/paddle/trainer_config_helpers/poolings.py
Unescape View File

@ -16,7 +16,8 @@
__all__ = [
"BasePoolingType", "MaxPooling", "AvgPooling", "MaxWithMaskPooling",
"CudnnMaxPooling", "CudnnAvgPooling", "SumPooling", "SquareRootNPooling"
"CudnnMaxPooling", "CudnnAvgPooling", "CudnnAvgInclPadPooling",
"SumPooling", "SquareRootNPooling"
]
@ -88,6 +89,16 @@ class CudnnAvgPooling(BasePoolingType):
BasePoolingType.__init__(self, "cudnn-avg-pool")
class CudnnAvgInclPadPooling(BasePoolingType):
"""
Cudnn average pooling only support GPU. Return the average value in the
pooling window taking into account the padding cells.
"""
def __init__(self):
BasePoolingType.__init__(self, "cudnn-avg-incl-pad-pool")
class AvgPooling(BasePoolingType):
"""
Average pooling.

Write Preview
Loading…
Cancel
Save