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avx_docs
qijun 9 years ago
parent db569f293e
commit e2c0713589
6 changed files with 81 additions and 78 deletions
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8
paddle/cuda/include/hl_cnn.h
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@ -91,7 +91,7 @@ extern void hl_expand_feature2col(
* @param[in] paddingH padding height.
* @param[in] paddingW padding width.
* @param[out] tgtData output data.
* @param[in] tgtStride output data stride.
* @param[in] tgtStride stride between output data samples.
*
*/
extern void hl_maxpool_forward(
@ -125,7 +125,7 @@ extern void hl_maxpool_forward(
* @param[in] paddingH padding height.
* @param[in] paddingW padding width.
* @param[out] targetGrad output grad.
* @param[in] outStride output grad data stride.
* @param[in] outStride stride between output data samples.
*
*/
extern void hl_maxpool_backward(
@ -157,7 +157,7 @@ extern void hl_maxpool_backward(
* @param[in] paddingH padding height.
* @param[in] paddingW padding width.
* @param[out] tgtData output data.
* @param[in] tgtStride output data stride.
* @param[in] tgtStride stride between output data samples.
*
*/
extern void hl_avgpool_forward(
@ -189,7 +189,7 @@ extern void hl_avgpool_forward(
* @param[in] scaleA scale.
* @param[in] scaleB scale.
* @param[out] backGrad output grad.
* @param[in] outStride output grad data stride.
* @param[in] outStride stride between output data samples.
*
*/
extern void hl_avgpool_backward(

22
paddle/gserver/layers/PoolProjection.cpp
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@ -34,9 +34,9 @@ PoolProjection* PoolProjection::create(const ProjectionConfig& config,
void MaxPoolProjection::forward() {
MatrixPtr inputV = in_->value;
MatrixPtr outV = out_->value;
outV->maxPoolForward(*inputV, imgSizeY_, imgSize_, channels_,
sizeX_, sizeY_, strideY_, stride_,
outputY_, outputX_, confPaddingY_, confPadding_);
outV->maxPoolForward(*inputV, imgSizeY_, imgSize_, channels_, sizeX_, sizeY_,
strideY_, stride_, outputY_, outputX_, confPaddingY_,
confPadding_);
}
void MaxPoolProjection::backward(const UpdateCallback& callback) {
@ -50,17 +50,16 @@ void MaxPoolProjection::backward(const UpdateCallback& callback) {
return;
}
inputGrad->maxPoolBackward(*inputV, imgSizeY_, imgSize_, *outGrad, *outV,
sizeX_, sizeY_,
strideY_, stride_, outputY_, outputX_, 1, 1,
confPaddingY_, confPadding_);
sizeX_, sizeY_, strideY_, stride_, outputY_,
outputX_, 1, 1, confPaddingY_, confPadding_);
}
void AvgPoolProjection::forward() {
MatrixPtr inputV = in_->value;
MatrixPtr outV = out_->value;
outV->avgPoolForward(*inputV, imgSizeY_, imgSize_, channels_,
sizeX_, sizeY_, strideY_, stride_,
outputY_, outputX_, confPaddingY_, confPadding_);
outV->avgPoolForward(*inputV, imgSizeY_, imgSize_, channels_, sizeX_, sizeY_,
strideY_, stride_, outputY_, outputX_, confPaddingY_,
confPadding_);
}
void AvgPoolProjection::backward(const UpdateCallback& callback) {
@ -73,9 +72,8 @@ void AvgPoolProjection::backward(const UpdateCallback& callback) {
return;
}
inputGrad->avgPoolBackward(*outputGrad, imgSizeY_, imgSize_,
sizeX_, sizeY_, strideY_, stride_,
outputY_, outputX_, 1, 1,
inputGrad->avgPoolBackward(*outputGrad, imgSizeY_, imgSize_, sizeX_, sizeY_,
strideY_, stride_, outputY_, outputX_, 1, 1,
confPaddingY_, confPadding_);
}
} // namespace paddle

1
paddle/gserver/layers/PoolProjection.h
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@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include "Projection.h"
#include "paddle/math/MathUtils.h"
namespace paddle {

14
paddle/gserver/layers/SpatialPyramidPoolLayer.cpp
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@ -56,8 +56,15 @@ ProjectionConfig SpatialPyramidPoolLayer::getConfig(size_t imgSizeW,
size_t SpatialPyramidPoolLayer::getSize() {
CHECK_EQ(inputLayers_.size(), 1UL);
size_t layerSize = 0;
const SppConfig& sppConf = config_.inputs(0).spp_conf();
imgSizeH_ = inputLayers_[0]->getOutput().getFrameHeight();
imgSizeW_ = inputLayers_[0]->getOutput().getFrameWidth();
if (imgSizeH_ == 0) {
imgSizeH_ = sppConf.has_img_size_y() ? sppConf.img_size_y() : imgSizeW_;
}
if (imgSizeW_ == 0) {
imgSizeW_ = sppConf.img_size();
}
size_t outputH = 1;
size_t outputW = (std::pow(4, pyramidHeight_) - 1) / (4 - 1);
@ -66,10 +73,10 @@ size_t SpatialPyramidPoolLayer::getSize() {
getOutput().setFrameHeight(outputH);
getOutput().setFrameWidth(outputW);
return layerSize;
}
bool SpatialPyramidPoolLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
Layer::init(layerMap, parameterMap);
@ -90,8 +97,8 @@ bool SpatialPyramidPoolLayer::init(const LayerMap& layerMap,
size_t endCol = 0;
for (size_t i = 0; i < pyramidHeight_; i++) {
poolProjections_.emplace_back(PoolProjection::create(
getConfig(imgSizeW_, imgSizeH_, channels_, i, poolType_),
nullptr, useGpu_));
getConfig(imgSizeW_, imgSizeH_, channels_, i, poolType_), nullptr,
useGpu_));
endCol += poolProjections_[i]->getOutputSize();
projCol_.push_back(std::make_pair(startCol, endCol));
startCol = endCol;
@ -125,4 +132,3 @@ void SpatialPyramidPoolLayer::backward(const UpdateCallback& callback) {
}
} // namespace paddle

112
python/paddle/trainer_config_helpers/layers.py
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@ -56,7 +56,7 @@ __all__ = ["full_matrix_projection", "AggregateLevel", "ExpandLevel",
'rank_cost', 'lambda_cost', 'huber_cost',
'block_expand_layer',
'maxout_layer', 'out_prod_layer', 'print_layer',
# 'spp_layer',
'spp_layer',
]
@ -112,7 +112,7 @@ class LayerType(object):
LINEAR_COMBINATION_LAYER = "convex_comb"
BLOCK_EXPAND = "blockexpand"
MAXOUT = "maxout"
# SPP_LAYER = "spp"
SPP_LAYER = "spp"
PRINT_LAYER = "print"
@ -1711,60 +1711,60 @@ def img_pool_layer(input, pool_size, name=None,
num_filters=num_channels)
# @wrap_name_default("spp")
# @layer_support()
# def spp_layer(input, name=None, num_channels=None, pool_type=None,
# pyramid_height=None, img_width=None, layer_attr=None):
# pass
# """
# Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.
# The details please refer to
# `Kaiming He's paper <https://arxiv.org/abs/1406.4729>`_.
# :param name: layer name.
# :type name: basestring
# :param input: layer's input.
# :type input: LayerOutput
# :param num_channels: number of input channel.
# :type num_channels: int
# :param pool_type: Pooling type. MaxPooling or AveragePooling. Default is MaxPooling.
# :type scale: BasePoolingType
# :param pyramid_height: pyramid height.
# :type pyramid_height: int
# :param img_width: the width of input feature map. If it is None, the input feature
# map should be square.
# :type img_width: int|None
# :param layer_attr: Extra Layer Attribute.
# :type layer_attr: ExtraLayerAttribute
# :return: LayerOutput object.
# :rtype: LayerOutput
# """
# if num_channels is None:
# assert input.num_filters is not None
# num_channels = input.num_filters
# if pool_type is None:
# pool_type = MaxPooling()
# elif isinstance(pool_type, AvgPooling):
# pool_type.name = 'avg'
# type_name = pool_type.name
# if (isinstance(pool_type, AvgPooling) or isinstance(pool_type, MaxPooling)):
# type_name += '-projection'
# Layer(
# name=name,
# type=LayerType.SPP_LAYER,
# inputs=Input(input.name,
# spp=SpatialPyramidPool(pool_type=type_name,
# channels=num_channels,
# pyramid_height=pyramid_height,
# img_width=img_width)
# ),
# **ExtraLayerAttribute.to_kwargs(layer_attr)
# )
# return LayerOutput(name, LayerType.SPP_LAYER, parents=[input],
# num_filters=num_channels)
@wrap_name_default("spp")
@layer_support()
def spp_layer(input, name=None, num_channels=None, pool_type=None,
pyramid_height=None, img_width=None, layer_attr=None):
pass
"""
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.
The details please refer to
`Kaiming He's paper <https://arxiv.org/abs/1406.4729>`_.
:param name: layer name.
:type name: basestring
:param input: layer's input.
:type input: LayerOutput
:param num_channels: number of input channel.
:type num_channels: int
:param pool_type: Pooling type. MaxPooling or AveragePooling. Default is MaxPooling.
:type scale: BasePoolingType
:param pyramid_height: pyramid height.
:type pyramid_height: int
:param img_width: the width of input feature map. If it is None, the input feature
map should be square.
:type img_width: int|None
:param layer_attr: Extra Layer Attribute.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
if num_channels is None:
assert input.num_filters is not None
num_channels = input.num_filters
if pool_type is None:
pool_type = MaxPooling()
elif isinstance(pool_type, AvgPooling):
pool_type.name = 'avg'
type_name = pool_type.name
if (isinstance(pool_type, AvgPooling) or isinstance(pool_type, MaxPooling)):
type_name += '-projection'
Layer(
name=name,
type=LayerType.SPP_LAYER,
inputs=Input(input.name,
spp=SpatialPyramidPool(pool_type=type_name,
channels=num_channels,
pyramid_height=pyramid_height,
img_width=img_width)
),
**ExtraLayerAttribute.to_kwargs(layer_attr)
)
return LayerOutput(name, LayerType.SPP_LAYER, parents=[input],
num_filters=num_channels)
def __img_norm_layer__(name, input, size, norm_type, scale, power,

2
python/paddle/trainer_config_helpers/tests/configs/generate_protostr.sh
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@ -11,11 +11,9 @@ test_sequence_pooling test_lstmemory_layer test_grumemory_layer
last_first_seq test_expand_layer test_ntm_layers test_hsigmoid
img_layers util_layers simple_rnn_layers unused_layers test_cost_layers
test_rnn_group shared_fc shared_lstm test_cost_layers_with_weight
# test_maxout test_bi_grumemory math_ops test_spp_layer)
test_maxout test_bi_grumemory math_ops test_spp_layer)
for conf in ${configs[*]}
do
echo "Generating " $conf

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