提交 353d26ce 编写于 作者: Q qingqing01

Fix conflicts.

......@@ -18,6 +18,11 @@ dynamic_lstm
.. autofunction:: paddle.v2.fluid.layers.dynamic_lstm
:noindex:
dynamic_gru
-----------
.. autofunction:: paddle.v2.fluid.layers.dynamic_gru
:noindex:
data
----
.. autofunction:: paddle.v2.fluid.layers.data
......@@ -500,6 +505,11 @@ swish
.. autofunction:: paddle.v2.fluid.layers.swish
:noindex:
im2sequence
------
.. autofunction:: paddle.v2.fluid.layers.im2sequence
:noindex:
edit_distance
---------------
.. autofunction:: paddle.v2.fluid.layers.edit_distance_error
......
......@@ -25,14 +25,14 @@
.. code-block:: bash
docker pull docker.paddlepaddle.org/paddle
docker pull docker.paddlepaddlehub.com/paddle
下载GPU版本(cuda8.0_cudnn5_avx_mkl)的Docker镜像:
.. code-block:: bash
docker pull paddlepaddle/paddle:latest-gpu
docker pull docker.paddlepaddle.org/paddle:latest-gpu
docker pull docker.paddlepaddlehub.com/paddle:latest-gpu
选择下载使用不同的BLAS库的Docker镜像:
......@@ -49,7 +49,7 @@
docker pull paddlepaddle/paddle:[tag]
# 比如:
docker pull docker.paddlepaddle.org/paddle:0.10.0-gpu
docker pull docker.paddlepaddlehub.com/paddle:0.11.0-gpu
.. _docker_run:
......
......@@ -26,14 +26,14 @@ For users in China, we provide a faster mirror:
.. code-block:: bash
docker pull docker.paddlepaddle.org/paddle
docker pull docker.paddlepaddlehub.com/paddle
Download GPU version (cuda8.0_cudnn5_avx_mkl) images:
.. code-block:: bash
docker pull paddlepaddle/paddle:latest-gpu
docker pull docker.paddlepaddle.org/paddle:latest-gpu
docker pull docker.paddlepaddlehub.com/paddle:latest-gpu
Choose between different BLAS version:
......@@ -53,7 +53,7 @@ and run:
docker pull paddlepaddle/paddle:[tag]
# i.e.
docker pull docker.paddlepaddle.org/paddle:0.10.0-gpu
docker pull docker.paddlepaddlehub.com/paddle:0.11.0-gpu
.. _docker_run:
......
......@@ -75,7 +75,7 @@ std::vector<VarDesc *> BlockDesc::AllVars() const {
OpDesc *BlockDesc::AppendOp() {
need_update_ = true;
ops_.emplace_back(new OpDesc());
ops_.emplace_back(new OpDesc(this));
return ops_.back().get();
}
......@@ -86,7 +86,7 @@ void BlockDesc::AppendAllocatedOp(std::unique_ptr<OpDesc> &&op_desc) {
OpDesc *BlockDesc::PrependOp() {
need_update_ = true;
ops_.emplace_front(new OpDesc());
ops_.emplace_front(new OpDesc(this));
return ops_.front().get();
}
......@@ -153,7 +153,7 @@ BlockDesc::BlockDesc(ProgramDesc *prog, proto::BlockDesc *desc)
vars_[var_desc.name()].reset(new VarDesc(var_desc));
}
for (const proto::OpDesc &op_desc : desc_->ops()) {
ops_.emplace_back(new OpDesc(op_desc, prog));
ops_.emplace_back(new OpDesc(op_desc, prog, this));
}
}
......@@ -162,7 +162,7 @@ BlockDesc::BlockDesc(const BlockDesc &other, proto::BlockDesc *desc,
: prog_(prog), desc_(desc) {
need_update_ = true;
for (auto &op : other.ops_) {
ops_.emplace_back(new OpDesc(*op));
ops_.emplace_back(new OpDesc(*op, this));
}
for (auto &it : other.vars_) {
......
......@@ -97,7 +97,7 @@ void OpDesc::CopyFrom(const OpDesc &op_desc) {
need_update_ = true;
}
OpDesc::OpDesc(const proto::OpDesc &desc, ProgramDesc *prog)
OpDesc::OpDesc(const proto::OpDesc &desc, ProgramDesc *prog, BlockDesc *block)
: desc_(desc), need_update_(false) {
// restore inputs_
int input_size = desc_.inputs_size();
......@@ -131,6 +131,7 @@ OpDesc::OpDesc(const proto::OpDesc &desc, ProgramDesc *prog)
attrs_[attr_name] = prog->MutableBlock(bid);
}
}
this->block_ = block;
}
proto::OpDesc *OpDesc::Proto() {
......
......@@ -25,7 +25,6 @@ namespace framework {
class BlockDesc;
class ProgramDesc;
class OpDesc {
public:
OpDesc() {}
......@@ -33,7 +32,14 @@ class OpDesc {
OpDesc(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const AttributeMap &attrs);
OpDesc(const proto::OpDesc &desc, ProgramDesc *prog);
OpDesc(const proto::OpDesc &desc, ProgramDesc *prog, BlockDesc *block);
explicit OpDesc(BlockDesc *block) : block_(block) {}
OpDesc(const OpDesc &other, BlockDesc *block) {
*this = other;
block_ = block;
}
void CopyFrom(const OpDesc &op_desc);
......@@ -117,6 +123,10 @@ class OpDesc {
void Flush();
BlockDesc *Block() { return this->block_; }
void SetBlock(BlockDesc *block) { this->block_ = block; }
private:
template <typename MapType>
static std::vector<typename MapType::key_type> MapKeys(const MapType &map) {
......@@ -129,6 +139,7 @@ class OpDesc {
}
proto::OpDesc desc_;
BlockDesc *block_; // not_own
// input arg name => input variable names
VariableNameMap inputs_;
// output arg name => output variable names
......
......@@ -66,6 +66,8 @@ class VarDesc {
std::string Name() const { return desc_.name(); }
void SetName(std::string name) { desc_.set_name(name); }
void SetShape(const std::vector<int64_t> &dims);
void SetDataType(proto::DataType data_type);
......
......@@ -12,19 +12,6 @@
// See the License for the specific language governing permissions and
// limitations under the License.
/*
Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#include <memory>
#include <string>
......
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/iou_similarity_op.h"
namespace paddle {
namespace operators {
class IOUSimilarityOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of IOUSimilarityOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Y"),
"Input(Y) of IOUSimilarityOp should not be null.");
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(x_dims.size(), 2UL, "The rank of Input(X) must be 2.");
PADDLE_ENFORCE_EQ(x_dims[1], 4UL, "The shape of X is [N, 4]");
PADDLE_ENFORCE_EQ(y_dims.size(), 2UL, "The rank of Input(Y) must be 2.");
PADDLE_ENFORCE_EQ(y_dims[1], 4UL, "The shape of Y is [M, 4]");
ctx->ShareLoD("X", /*->*/ "Out");
ctx->SetOutputDim("Out", framework::make_ddim({x_dims[0], y_dims[0]}));
}
};
class IOUSimilarityOpMaker : public framework::OpProtoAndCheckerMaker {
public:
IOUSimilarityOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(LoDTensor, default LoDTensor<float>) "
"Box list X is a 2-D LoDTensor with shape [N, 4] holds N boxes, "
"each box is represented as [xmin, ymin, xmax, ymax], "
"the shape of X is [N, 4]. [xmin, ymin] is the left top "
"coordinate of the box if the input is image feature map, they "
"are close to the origin of the coordinate system. "
"[xmax, ymax] is the right bottom coordinate of the box. "
"This tensor can contain LoD information to represent a batch "
"of inputs. One instance of this batch can contain different "
"numbers of entities.");
AddInput("Y",
"(Tensor, default Tensor<float>) "
"Box list Y holds M boxes, each box is represented as "
"[xmin, ymin, xmax, ymax], the shape of X is [N, 4]. "
"[xmin, ymin] is the left top coordinate of the box if the "
"input is image feature map, and [xmax, ymax] is the right "
"bottom coordinate of the box.");
AddOutput("Out",
"(LoDTensor, the lod is same as input X) The output of "
"iou_similarity op, a tensor with shape [N, M] "
"representing pairwise iou scores.");
AddComment(R"DOC(
IOU Similarity Operator.
Computes intersection-over-union (IOU) between two box lists.
Box list 'X' should be a LoDTensor and 'Y' is a common Tensor,
boxes in 'Y' are shared by all instance of the batched inputs of X.
Given two boxes A and B, the calculation of IOU is as follows:
$$
IOU(A, B) =
\frac{area(A\cap B)}{area(A)+area(B)-area(A\cap B)}
$$
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(iou_similarity, ops::IOUSimilarityOp,
ops::IOUSimilarityOpMaker);
REGISTER_OP_CPU_KERNEL(
iou_similarity,
ops::IOUSimilarityKernel<paddle::platform::CPUDeviceContext, float>,
ops::IOUSimilarityKernel<paddle::platform::CPUDeviceContext, double>);
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/iou_similarity_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
iou_similarity,
ops::IOUSimilarityKernel<paddle::platform::CUDADeviceContext, float>,
ops::IOUSimilarityKernel<paddle::platform::CUDADeviceContext, double>);
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/op_registry.h"
#include "paddle/platform/for_range.h"
template <typename T>
inline HOSTDEVICE T IOUSimilarity(T xmin1, T ymin1, T xmax1, T ymax1, T xmin2,
T ymin2, T xmax2, T ymax2) {
constexpr T zero = static_cast<T>(0);
T area1 = (ymax1 - ymin1) * (xmax1 - xmin1);
T area2 = (ymax2 - ymin2) * (xmax2 - xmin2);
T inter_xmax = xmax1 > xmax2 ? xmax2 : xmax1;
T inter_ymax = ymax1 > ymax2 ? ymax2 : ymax1;
T inter_xmin = xmin1 > xmin2 ? xmin1 : xmin2;
T inter_ymin = ymin1 > ymin2 ? ymin1 : ymin2;
T inter_height = inter_ymax - inter_ymin;
T inter_width = inter_xmax - inter_xmin;
inter_height = inter_height > zero ? inter_height : zero;
inter_width = inter_width > zero ? inter_width : zero;
T inter_area = inter_width * inter_height;
T union_area = area1 + area2 - inter_area;
T sim_score = inter_area / union_area;
return sim_score;
}
template <typename T>
struct IOUSimilarityFunctor {
IOUSimilarityFunctor(const T* x, const T* y, T* z, int cols)
: x_(x), y_(y), z_(z), cols_(static_cast<size_t>(cols)) {}
inline HOSTDEVICE void operator()(size_t row_id) const {
T x_min1 = x_[row_id * 4];
T y_min1 = x_[row_id * 4 + 1];
T x_max1 = x_[row_id * 4 + 2];
T y_max1 = x_[row_id * 4 + 3];
for (size_t i = 0; i < cols_; ++i) {
T x_min2 = y_[i * 4];
T y_min2 = y_[i * 4 + 1];
T x_max2 = y_[i * 4 + 2];
T y_max2 = y_[i * 4 + 3];
T sim = IOUSimilarity(x_min1, y_min1, x_max1, y_max1, x_min2, y_min2,
x_max2, y_max2);
z_[row_id * cols_ + i] = sim;
}
}
const T* x_;
const T* y_;
T* z_;
const size_t cols_;
};
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class IOUSimilarityKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const framework::LoDTensor* in_x = ctx.Input<framework::LoDTensor>("X");
const framework::Tensor* in_y = ctx.Input<framework::Tensor>("Y");
framework::LoDTensor* out = ctx.Output<framework::LoDTensor>("Out");
int x_n = in_x->dims()[0];
int y_n = in_y->dims()[0];
IOUSimilarityFunctor<T> functor(in_x->data<T>(), in_y->data<T>(),
out->mutable_data<T>(ctx.GetPlace()), y_n);
platform::ForRange<DeviceContext> for_range(
static_cast<const DeviceContext&>(ctx.device_context()), x_n);
for_range(functor);
}
}; // namespace operators
} // namespace operators
} // namespace paddle
......@@ -124,7 +124,8 @@ class NCEOpMaker : public framework::OpProtoAndCheckerMaker {
"This attribute only be used in unitest. Classes "
"in this list wiil be used as negative classes "
"for every samples. Under normal conditions, "
"user should avoid setting this attribute.");
"user should avoid setting this attribute.")
.SetDefault({});
AddComment(R"DOC(
Compute and return the noise-contrastive estimation training loss.
See [Noise-contrastive estimation: A new estimation principle for unnormalized statistical models](http://www.jmlr.org/proceedings/papers/v9/gutmann10a/gutmann10a.pdf).
......
......@@ -197,7 +197,8 @@ class NCEGradKernel : public framework::OpKernel<T> {
// get d_x
auto d_x = context.Output<Tensor>(framework::GradVarName("Input"));
if (d_x != nullptr) {
d_x->mutable_data<T>(context.GetPlace());
auto* d_x_data = d_x->mutable_data<T>(context.GetPlace());
std::fill(d_x_data, d_x_data + d_x->numel(), 0.0);
auto d_x_matrix = EigenMatrix<T>::From(*d_x);
auto w_matrix = EigenMatrix<T>::From(*(context.Input<Tensor>("Weight")));
for (int64_t i = 0; i < sample_labels->numel(); ++i) {
......
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/prior_box_op.h"
namespace paddle {
namespace operators {
class PriorBoxOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input(Input) of PriorBoxOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Image"),
"Input(Image) of PriorBoxOp should not be null.");
auto image_dims = ctx->GetInputDim("Image");
auto input_dims = ctx->GetInputDim("Input");
PADDLE_ENFORCE(image_dims.size() == 4, "The layout of image is NCHW.");
PADDLE_ENFORCE(input_dims.size() == 4, "The layout of input is NCHW.");
PADDLE_ENFORCE_LT(input_dims[2], image_dims[2],
"The height of input must smaller than image.");
PADDLE_ENFORCE_LT(input_dims[3], image_dims[3],
"The width of input must smaller than image.");
auto min_sizes = ctx->Attrs().Get<std::vector<int>>("min_sizes");
auto max_sizes = ctx->Attrs().Get<std::vector<int>>("max_sizes");
auto variances = ctx->Attrs().Get<std::vector<float>>("variances");
auto aspect_ratios = ctx->Attrs().Get<std::vector<float>>("aspect_ratios");
bool flip = ctx->Attrs().Get<bool>("flip");
PADDLE_ENFORCE_GT(min_sizes.size(), 0,
"Size of min_sizes must be at least 1.");
for (size_t i = 0; i < min_sizes.size(); ++i) {
PADDLE_ENFORCE_GT(min_sizes[i], 0, "min_sizes[%d] must be positive.", i);
}
std::vector<float> aspect_ratios_vec;
ExpandAspectRatios(aspect_ratios, flip, aspect_ratios_vec);
int num_priors = aspect_ratios_vec.size() * min_sizes.size();
if (max_sizes.size() > 0) {
PADDLE_ENFORCE_EQ(max_sizes.size(), min_sizes.size(),
"The number of min_size and max_size must be equal.");
for (size_t i = 0; i < min_sizes.size(); ++i) {
PADDLE_ENFORCE_GT(max_sizes[i], min_sizes[i],
"max_size[%d] must be greater than min_size[%d].", i,
i);
num_priors += 1;
}
}
PADDLE_ENFORCE_EQ(variances.size(), 4, "Must and only provide 4 variance.");
for (size_t i = 0; i < variances.size(); ++i) {
PADDLE_ENFORCE_GT(variances[i], 0.0,
"variance[%d] must be greater than 0.", i);
}
const float step_h = ctx->Attrs().Get<float>("step_h");
PADDLE_ENFORCE_GT(step_h, 0.0, "step_h should be larger than 0.");
const float step_w = ctx->Attrs().Get<float>("step_w");
PADDLE_ENFORCE_GT(step_w, 0.0, "step_w should be larger than 0.");
std::vector<int64_t> dim_vec(4);
dim_vec[0] = input_dims[2];
dim_vec[1] = input_dims[3];
dim_vec[2] = num_priors;
dim_vec[3] = 4;
ctx->SetOutputDim("Boxes", framework::make_ddim(dim_vec));
ctx->SetOutputDim("Variances", framework::make_ddim(dim_vec));
}
};
class PriorBoxOpMaker : public framework::OpProtoAndCheckerMaker {
public:
PriorBoxOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Input",
"(Tensor, default Tensor<float>), "
"the input feature data of PriorBoxOp, The layout is NCHW.");
AddInput("Image",
"(Tensor, default Tensor<float>), "
"the input image data of PriorBoxOp, The layout is NCHW.");
AddOutput("Boxes",
"(Tensor, default Tensor<float>), the output prior boxes of "
"PriorBoxOp. The layout is [H, W, num_priors, 4]. "
"H is the height of input, W is the width of input, num_priors "
"is the box count of each position.");
AddOutput("Variances",
"(Tensor, default Tensor<float>), the expanded variances of "
"PriorBoxOp. The layout is [H, W, num_priors, 4]. "
"H is the height of input, W is the width of input, num_priors "
"is the box count of each position.");
AddAttr<std::vector<int>>("min_sizes", "(vector<int>) ",
"List of min sizes of generated prior boxes.");
AddAttr<std::vector<int>>("max_sizes", "(vector<int>) ",
"List of max sizes of generated prior boxes.");
AddAttr<std::vector<float>>(
"aspect_ratios", "(vector<float>) ",
"List of aspect ratios of generated prior boxes.");
AddAttr<std::vector<float>>(
"variances", "(vector<float>) ",
"List of variances to be encoded in prior boxes.");
AddAttr<bool>("flip", "(bool) ", "Whether to flip aspect ratios.")
.SetDefault(true);
AddAttr<bool>("clip", "(bool) ", "Whether to clip out-of-boundary boxes.")
.SetDefault(true);
AddAttr<float>("step_w",
"Prior boxes step across width, 0 for auto calculation.")
.SetDefault(0.0);
AddAttr<float>("step_h",
"Prior boxes step across height, 0 for auto calculation.")
.SetDefault(0.0);
AddAttr<float>("offset",
"(float) "
"Prior boxes center offset.")
.SetDefault(0.5);
AddComment(R"DOC(
Prior box operator
Generate prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
Each position of the input produce N prior boxes, N is determined by
the count of min_sizes, max_sizes and aspect_ratios, The size of the
box is in range(min_size, max_size) interval, which is generated in
sequence according to the aspect_ratios.
Please get more information from the following papers:
https://arxiv.org/abs/1512.02325.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(prior_box, ops::PriorBoxOp, ops::PriorBoxOpMaker);
REGISTER_OP_CPU_KERNEL(
prior_box, ops::PriorBoxOpKernel<paddle::platform::CPUPlace, float>,
ops::PriorBoxOpKernel<paddle::platform::CPUPlace, double>);
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/platform/transform.h"
namespace paddle {
namespace operators {
inline void ExpandAspectRatios(const std::vector<float>& input_aspect_ratior,
bool flip,
std::vector<float>& output_aspect_ratior) {
constexpr float epsilon = 1e-6;
output_aspect_ratior.clear();
output_aspect_ratior.push_back(1.);
for (size_t i = 0; i < input_aspect_ratior.size(); ++i) {
float ar = input_aspect_ratior[i];
bool already_exist = false;
for (size_t j = 0; j < output_aspect_ratior.size(); ++j) {
if (fabs(ar - output_aspect_ratior[j]) < epsilon) {
already_exist = true;
break;
}
}
if (!already_exist) {
output_aspect_ratior.push_back(ar);
if (flip) {
output_aspect_ratior.push_back(1. / ar);
}
}
}
}
template <typename T>
struct ClipFunctor {
HOSTDEVICE T operator()(T in) const {
return std::min<T>(std::max<T>(in, 0.), 1.);
}
};
template <typename Place, typename T>
class PriorBoxOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<paddle::framework::Tensor>("Input");
auto* image = ctx.Input<paddle::framework::Tensor>("Image");
auto* boxes = ctx.Output<paddle::framework::Tensor>("Boxes");
auto* vars = ctx.Output<paddle::framework::Tensor>("Variances");
auto min_sizes = ctx.Attr<std::vector<int>>("min_sizes");
auto max_sizes = ctx.Attr<std::vector<int>>("max_sizes");
auto input_aspect_ratio = ctx.Attr<std::vector<float>>("aspect_ratios");
auto variances = ctx.Attr<std::vector<float>>("variances");
auto flip = ctx.Attr<bool>("flip");
auto clip = ctx.Attr<bool>("clip");
std::vector<float> aspect_ratios;
ExpandAspectRatios(input_aspect_ratio, flip, aspect_ratios);
T step_w = static_cast<T>(ctx.Attr<float>("step_w"));
T step_h = static_cast<T>(ctx.Attr<float>("step_h"));
T offset = static_cast<T>(ctx.Attr<float>("offset"));
auto img_width = image->dims()[3];
auto img_height = image->dims()[2];
auto feature_width = input->dims()[3];
auto feature_height = input->dims()[2];
T step_width, step_height;
if (step_w == 0 || step_h == 0) {
step_width = static_cast<T>(img_width) / feature_width;
step_height = static_cast<T>(img_height) / feature_height;
} else {
step_width = step_w;
step_height = step_h;
}
int num_priors = aspect_ratios.size() * min_sizes.size();
if (max_sizes.size() > 0) {
num_priors += max_sizes.size();
}
boxes->mutable_data<T>(ctx.GetPlace());
vars->mutable_data<T>(ctx.GetPlace());
auto e_boxes = framework::EigenTensor<T, 4>::From(*boxes);
for (int h = 0; h < feature_height; ++h) {
for (int w = 0; w < feature_width; ++w) {
T center_x = (w + offset) * step_width;
T center_y = (h + offset) * step_height;
T box_width, box_height;
int idx = 0;
for (size_t s = 0; s < min_sizes.size(); ++s) {
int min_size = min_sizes[s];
// first prior: aspect_ratio = 1, size = min_size
box_width = box_height = min_size;
// xmin
e_boxes(h, w, idx, 0) = (center_x - box_width / 2.) / img_width;
// ymin
e_boxes(h, w, idx, 1) = (center_y - box_height / 2.) / img_height;
// xmax
e_boxes(h, w, idx, 2) = (center_x + box_width / 2.) / img_width;
// ymax
e_boxes(h, w, idx, 3) = (center_y + box_height / 2.) / img_height;
idx++;
if (max_sizes.size() > 0) {
int max_size = max_sizes[s];
// second prior: aspect_ratio = 1,
// size = sqrt(min_size * max_size)
box_width = box_height = sqrt(min_size * max_size);
// xmin
e_boxes(h, w, idx, 0) = (center_x - box_width / 2.) / img_width;
// ymin
e_boxes(h, w, idx, 1) = (center_y - box_height / 2.) / img_height;
// xmax
e_boxes(h, w, idx, 2) = (center_x + box_width / 2.) / img_width;
// ymax
e_boxes(h, w, idx, 3) = (center_y + box_height / 2.) / img_height;
idx++;
}
// rest of priors
for (size_t r = 0; r < aspect_ratios.size(); ++r) {
float ar = aspect_ratios[r];
if (fabs(ar - 1.) < 1e-6) {
continue;
}
box_width = min_size * sqrt(ar);
box_height = min_size / sqrt(ar);
// xmin
e_boxes(h, w, idx, 0) = (center_x - box_width / 2.) / img_width;
// ymin
e_boxes(h, w, idx, 1) = (center_y - box_height / 2.) / img_height;
// xmax
e_boxes(h, w, idx, 2) = (center_x + box_width / 2.) / img_width;
// ymax
e_boxes(h, w, idx, 3) = (center_y + box_height / 2.) / img_height;
idx++;
}
}
}
}
if (clip) {
platform::Transform<platform::CPUDeviceContext> trans;
ClipFunctor<T> clip_func;
trans(ctx.template device_context<platform::CPUDeviceContext>(),
boxes->data<T>(), boxes->data<T>() + boxes->numel(),
boxes->data<T>(), clip_func);
}
framework::Tensor var_t;
var_t.mutable_data<T>(
framework::make_ddim({1, static_cast<int>(variances.size())}),
ctx.GetPlace());
auto var_et = framework::EigenTensor<T, 2>::From(var_t);
for (size_t i = 0; i < variances.size(); ++i) {
var_et(0, i) = variances[i];
}
int box_num = feature_height * feature_width * num_priors;
auto var_dim = vars->dims();
vars->Resize({box_num, static_cast<int>(variances.size())});
auto e_vars = framework::EigenMatrix<T, Eigen::RowMajor>::From(*vars);
e_vars = var_et.broadcast(Eigen::DSizes<int, 2>(box_num, 1));
vars->Resize(var_dim);
}
}; // namespace operators
} // namespace operators
} // namespace paddle
......@@ -212,6 +212,7 @@ void BindVarDsec(py::module &m) {
return name;
},
py::return_value_policy::reference)
.def("set_name", &VarDesc::SetName)
.def("set_shape", &VarDesc::SetShape)
.def("set_dtype", &VarDesc::SetDataType)
.def("shape", &VarDesc::Shape, py::return_value_policy::reference)
......@@ -280,7 +281,8 @@ void BindOpDesc(py::module &m) {
.def("check_attrs", &OpDesc::CheckAttrs)
.def("infer_shape", &OpDesc::InferShape)
.def("infer_var_type", &OpDesc::InferVarType)
.def("serialize_to_string", SerializeMessage<OpDesc>);
.def("serialize_to_string", SerializeMessage<OpDesc>)
.def("block", &OpDesc::Block, py::return_value_policy::reference);
}
} // namespace pybind
......
......@@ -305,9 +305,9 @@ def get_dict(lang, dict_size, reverse=False):
dict_path = os.path.join(paddle.v2.dataset.common.DATA_HOME,
"wmt16/%s_%d.dict" % (lang, dict_size))
assert (os.path.exists(dict_path), "Word dictionary does not exist. "
"Please invoke paddle.dataset.wmt16.train/test/validation "
"first to build the dictionary.")
assert os.path.exists(dict_path), "Word dictionary does not exist. "
"Please invoke paddle.dataset.wmt16.train/test/validation first "
"to build the dictionary."
tar_file = os.path.join(paddle.v2.dataset.common.DATA_HOME, "wmt16.tar.gz")
return __load_dict(tar_file, dict_size, lang, reverse)
......
......@@ -19,12 +19,14 @@ from ..layer_helper import LayerHelper
from ..initializer import Normal, Constant
from ..framework import Variable
from ..param_attr import ParamAttr
from layer_function_generator import autodoc
from tensor import concat
__all__ = [
'fc',
'embedding',
'dynamic_lstm',
'dynamic_gru',
'gru_unit',
'linear_chain_crf',
'crf_decoding',
......@@ -57,6 +59,8 @@ __all__ = [
'warpctc',
'sequence_reshape',
'transpose',
'im2sequence',
'nce',
]
......@@ -366,6 +370,113 @@ def dynamic_lstm(input,
return hidden, cell
def dynamic_gru(input,
size,
param_attr=None,
bias_attr=None,
is_reverse=False,
gate_activation='sigmoid',
candidate_activation='tanh',
h_0=None):
"""
**Dynamic GRU Layer**
Refer to `Empirical Evaluation of Gated Recurrent Neural Networks on
Sequence Modeling <https://arxiv.org/abs/1412.3555>`_
The formula is as follows:
.. math::
u_t & = act_g(W_{ux}x_{t} + W_{uh}h_{t-1} + b_u)
r_t & = act_g(W_{rx}x_{t} + W_{rh}h_{t-1} + b_r)
\\tilde{h_t} & = act_c(W_{cx}x_{t} + W_{ch}(r_t \odot h_{t-1}) + b_c)
h_t & = (1-u_t) \odot h_{t-1} + u_t \odot \\tilde{h_t}
The :math:`\odot` is the element-wise product of the vectors. :math:`act_g`
is the update gate and reset gate activation function and :math:`sigmoid`
is usually used for it. :math:`act_c` is the activation function for
candidate hidden state and :math:`tanh` is usually used for it.
Note that these :math:`W_{ux}x_{t}, W_{rx}x_{t}, W_{cx}x_{t}` operations on
the input :math:`x_{t}` are NOT included in this operator. Users can choose
to use fully-connect layer before GRU layer.
Args:
input(Variable): The input of dynamic_gru layer, which supports
variable-time length input sequence. The underlying tensor in this
Variable is a matrix with shape :math:`(T \\times 3D)`, where
:math:`T` is the total time steps in this mini-batch, :math:`D`
is the hidden size.
size(int): The dimension of the gru cell.
param_attr(ParamAttr|None): The parameter attribute for the learnable
hidden-hidden weight matrix. Note:
- The shape of the weight matrix is :math:`(T \\times 3D)`, where
:math:`D` is the hidden size.
- All elements in the weight matrix can be divided into two parts.
The first part are weights of the update gate and reset gate with
shape :math:`(D \\times 2D)`, and the second part are weights for
candidate hidden state with shape :math:`(D \\times D)`.
bias_attr(ParamAttr): The parameter attribute for learnable the
hidden-hidden bias.
is_reverse(bool): Whether to compute reversed GRU, default
:attr:`False`.
gate_activation(str): The activation for update gate and reset gate.
Choices = ["sigmoid", "tanh", "relu", "identity"], default "sigmoid".
activation(str): The activation for candidate hidden state.
Choices = ["sigmoid", "tanh", "relu", "identity"], default "tanh".
Returns:
Variable: The hidden state of GRU. The shape is (T \\times D), and lod \
is the same with the input.
Examples:
.. code-block:: python
hidden_dim = 512
x = fluid.layers.fc(input=data, size=hidden_dim * 3)
hidden = fluid.layers.dynamic_gru(input=x, dim=hidden_dim)
"""
helper = LayerHelper('gru', **locals())
dtype = helper.input_dtype()
weight = helper.create_parameter(
attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
bias = helper.create_parameter(
attr=helper.bias_attr, shape=[1, 3 * size], dtype=dtype, is_bias=True)
inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
if h_0 != None:
assert h_0.shape == (
size, size), 'The shape of h0 should be(%d, %d)' % (size, size)
inputs['h0'] = h_0
hidden = helper.create_tmp_variable(dtype)
batch_gate = helper.create_tmp_variable(dtype)
batch_reset_hidden_prev = helper.create_tmp_variable(dtype)
batch_hidden = helper.create_tmp_variable(dtype)
helper.append_op(
type='gru',
inputs=inputs,
outputs={
'Hidden': hidden,
'BatchGate': batch_gate,
'BatchResetHiddenPrev': batch_reset_hidden_prev,
'BatchHidden': batch_hidden
},
attrs={
'is_reverse': is_reverse,
'gate_activation': gate_activation,
'activation': candidate_activation
})
return hidden
def gru_unit(input,
hidden,
size,
......@@ -2190,6 +2301,61 @@ def sequence_reshape(input, new_dim):
return out
@autodoc()
def nce(input,
label,
num_total_classes,
sample_weight=None,
param_attr=None,
bias_attr=None,
num_neg_samples=None):
helper = LayerHelper('nce', **locals())
assert isinstance(input, Variable)
dim = input.shape[1]
assert isinstance(label, Variable)
num_true_class = label.shape[1]
w = helper.create_parameter(
attr=helper.param_attr,
shape=[num_total_classes, dim],
is_bias=False,
dtype=input.dtype)
b = helper.create_parameter(
attr=helper.bias_attr,
shape=[num_total_classes, 1],
is_bias=True,
dtype=input.dtype)
cost = helper.create_tmp_variable(dtype=input.dtype)
sample_logits = helper.create_tmp_variable(dtype=input.dtype)
sample_labels = helper.create_tmp_variable(dtype=label.dtype)
if num_neg_samples is None:
num_neg_samples = 10
else:
num_neg_samples = int(num_neg_samples)
attrs = {
'num_total_classes': int(num_total_classes),
'num_neg_samples': num_neg_samples
}
helper.append_op(
type='nce',
inputs={
'Input': input,
'Label': label,
'Weight': w,
'Bias': b,
'SampleWeight': sample_weight if sample_weight is not None else []
},
outputs={
'Cost': cost,
'SampleLogits': sample_logits,
'SampleLabels': sample_labels
},
attrs=attrs)
return cost / (num_neg_samples + 1)
def transpose(x, perm, name=None):
"""
**transpose Layer**
......@@ -2226,3 +2392,128 @@ def transpose(x, perm, name=None):
outputs={'Out': [out]},
attrs={'axis': perm})
return out
def im2sequence(input, filter_size=1, stride=1, padding=0, name=None):
"""
Extracts image patches from the input tensor to form a tensor of shape
{input.batch_size * output_height * output_width, filter_size_H *
filter_size_W * input.channels} which is similar with im2col.
This op use filter / kernel to scan images and convert these images to
sequences. After expanding, the number of time step are
output_height * output_width for an image, in which output_height and
output_width are calculated by below equation:
.. math::
output\_size = 1 + \
(2 * padding + img\_size - block\_size + stride - 1) / stride
And the dimension of each time step is block_y * block_x * input.channels.
Args:
input (Variable): The input should be a tensor in NCHW format.
filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_H, filter_size_W).
Otherwise, the filter will be a square.
stride(int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride. Default: stride = 1.
padding(int|tuple): The padding size. If padding is a tuple, it can
contain two integers like (padding_H, padding_W) which means
padding_up = padding_down = padding_H and
padding_left = padding_right = padding_W. Or it can use
(padding_up, padding_left, padding_down, padding_right) to indicate
paddings of four direction. Otherwise, a scalar padding means
padding_up = padding_down = padding_left = padding_right = padding
Default: padding = 0.
name (int): The name of this layer. It is optional.
Returns:
output: The output is a LoDTensor with shape
{input.batch_size * output_height * output_width,
filter_size_H * filter_size_W * input.channels}.
If we regard output as a matrix, each row of this matrix is
a step of a sequence.
Examples:
As an example:
.. code-block:: text
Given:
x = [[[[ 6. 2. 1.]
[ 8. 3. 5.]
[ 0. 2. 6.]]
[[ 2. 4. 4.]
[ 6. 3. 0.]
[ 6. 4. 7.]]]
[[[ 6. 7. 1.]
[ 5. 7. 9.]
[ 2. 4. 8.]]
[[ 1. 2. 1.]
[ 1. 3. 5.]
[ 9. 0. 8.]]]]
x.dims = {2, 2, 3, 3}
And:
filter = [2, 2]
stride = [1, 1]
padding = [0, 0]
Then:
output.data = [[ 6. 2. 8. 3. 2. 4. 6. 3.]
[ 2. 1. 3. 5. 4. 4. 3. 0.]
[ 8. 3. 0. 2. 6. 3. 6. 4.]
[ 3. 5. 2. 6. 3. 0. 4. 7.]
[ 6. 7. 5. 7. 1. 2. 1. 3.]
[ 7. 1. 7. 9. 2. 1. 3. 5.]
[ 5. 7. 2. 4. 1. 3. 9. 0.]
[ 7. 9. 4. 8. 3. 5. 0. 8.]]
output.dims = {8, 9}
output.lod = [[0, 4, 8]]
The simple usage is:
.. code-block:: python
output = fluid.layers.im2sequence(input=layer, stride=[1, 1], filter_size=[2, 2])
"""
if isinstance(filter_size, int):
filter_size = [filter_size, filter_size]
if isinstance(stride, int):
stride = [stride, stride]
if isinstance(padding, int):
padding = [padding, padding]
if len(padding) == 2:
padding.append(padding[0])
padding.append(padding[1])
helper = LayerHelper('im2sequence', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
helper.append_op(
type='im2sequence',
inputs={'X': input},
outputs={'Out': out},
attrs={
'kernels': filter_size,
'strides': stride,
'paddings': padding,
})
return out
......@@ -31,10 +31,12 @@ dtype_to_size = {
class ControlFlowGraph(object):
def __init__(self, Program):
def __init__(self, Program, ops, forward_num):
self._program = Program
self._succesors = defaultdict(set)
self._presucessors = defaultdict(set)
self._ops = ops
self._forward_num = forward_num
self._successors = defaultdict(set)
self._presuccessors = defaultdict(set)
self._uses = defaultdict(set)
self._defs = defaultdict(set)
self._live_in = defaultdict(set)
......@@ -45,25 +47,16 @@ class ControlFlowGraph(object):
self._add(node1, node2)
def _add(self, node1, node2):
self._succesors[node1].add(node2)
self._presucessors[node2].add(node1)
self._successors[node1].add(node2)
self._presuccessors[node2].add(node1)
def _build_graph(self):
program_desc = self._program.get_desc()
block_size = program_desc.num_blocks()
# TODO(qijun) handle Program with if/while operators
self.global_block_desc = program_desc.block(0)
self.op_size = self.global_block_desc.op_size()
self.op_size = len(self._ops)
op_node_connections = [(i, i + 1) for i in range(self.op_size - 1)]
self._add_connections(op_node_connections)
self.ops = [self.global_block_desc.op(i) for i in range(self.op_size)]
for i in range(self.op_size):
self._uses[i].update(self.ops[i].input_arg_names())
self._defs[i].update(self.ops[i].output_arg_names())
self._uses[i].update(self._ops[i].input_arg_names())
self._defs[i].update(self._ops[i].output_arg_names())
def _update_graph(self, old_name, new_name, begin_idx=0):
for i in range(begin_idx, self.op_size):
......@@ -103,7 +96,7 @@ class ControlFlowGraph(object):
live_out[i] = set(self._live_out[i])
self._live_in[i] = self._uses[i] | (
self._live_out[i] - self._defs[i])
for s in self._succesors[i]:
for s in self._successors[i]:
self._live_out[i] |= self._live_in[s]
if self._reach_fixed_point(live_in, live_out):
......@@ -113,39 +106,76 @@ class ControlFlowGraph(object):
u = a & b
return a - u, b - u
def _has_var(self, block_desc, var_name, is_forward):
if is_forward:
return block_desc.has_var(str(var_name))
else:
return block_desc.has_var_recursive(str(var_name))
def _find_var(self, block_desc, var_name, is_forward):
if is_forward:
return block_desc.find_var(str(var_name))
else:
return block_desc.find_var_recursive(str(var_name))
def memory_optimize(self):
def check_var_validity(block_desc, x, is_forward):
if str(x) == "@EMPTY@":
return False
if not self._has_var(block_desc, x, is_forward):
return False
if self._find_var(block_desc, x, is_forward).persistable():
return False
if self._find_var(
block_desc, x,
is_forward).type() != core.VarDesc.VarType.LOD_TENSOR:
return False
return True
self._build_graph()
self._dataflow_analyze()
self.pool = []
for i in range(self.op_size):
op = self._ops[i]
if op.type() == "while" or op.type() == "while_grad":
continue
block_desc = op.block()
is_forward = i < self._forward_num
if self.pool:
out_pair = [(x, self.global_block_desc.var(str(x)).shape())
for x in self._defs[i]]
defs_can_optimize = filter(
lambda x: check_var_validity(block_desc, x, is_forward),
self._defs[i])
out_pair = [
(x, self._find_var(block_desc, x, is_forward).shape())
for x in defs_can_optimize
]
for x, x_shape in out_pair:
if not self.global_block_desc.var(str(x)).persistable():
for index, cache_pair in enumerate(self.pool):
cache_var = cache_pair[0]
cache_shape = cache_pair[1]
if x_shape == cache_shape:
x_dtype = self.global_block_desc.var(str(
x)).dtype()
cache_dtype = self.global_block_desc.var(
str(cache_var)).dtype()
for index, cache_pair in enumerate(self.pool):
cache_var = cache_pair[0]
cache_shape = cache_pair[1]
if x_shape == cache_shape:
if self._has_var(block_desc, cache_var, is_forward):
x_dtype = self._find_var(block_desc, x,
is_forward).dtype()
cache_dtype = self._find_var(
block_desc, cache_var, is_forward).dtype()
# TODO(qijun): actually, we should compare dtype_to_size[x_dtype]
# and dtype_to_size[cache_dtype]
if x_dtype == cache_dtype:
print(
("Hit Cache !!!! cache pool index "
"is %d, var name is %s, "
"cached var name is %s, "
"var shape is %s ") %
(index, x, cache_var, str(cache_shape)))
print(("Hit Cache !!!! cache pool index "
"is %d, var name is %s, "
"cached var name is %s, "
"var shape is %s ") %
(index, x, cache_var,
str(cache_shape)))
self.pool.pop(index)
if x == cache_var:
break
_rename_arg_(
self.ops, x, cache_var, begin_idx=i)
self._program.current_block().var(str(
x)).desc = self.global_block_desc.var(
str(cache_var))
self._ops, x, cache_var, begin_idx=i)
self._program.block(block_desc.id).var(
str(x)).desc = self._find_var(
block_desc, cache_var, is_forward)
self._update_graph(
x, cache_var, begin_idx=i)
break
......@@ -153,20 +183,70 @@ class ControlFlowGraph(object):
in_diff, out_diff = self._get_diff(self._live_in[i],
self._live_out[i])
can_optimize = filter(
lambda x: not self.global_block_desc.var(str(x)).persistable(),
lambda x: check_var_validity(block_desc, x, is_forward),
in_diff)
if can_optimize:
for var_name in can_optimize:
self.pool.append(
(var_name,
self.global_block_desc.var(str(var_name)).shape()))
def get_program(self):
return self._program
self.pool.append((var_name, self._find_var(
block_desc, var_name, is_forward).shape()))
def get_cfgs(input_program):
ops_list = []
pdesc = input_program.get_desc()
block_desc = pdesc.block(0)
op_size = block_desc.op_size()
# Get global block ops
ops_list.append(([block_desc.op(i) for i in range(op_size)], op_size))
while_sub_block_ids = []
while_grad_sub_block_ids = []
while_pair = []
for i in range(op_size):
op = block_desc.op(i)
if op.type() == "while":
while_sub_block_ids.append(op.attr("sub_block").id)
elif op.type() == "while_grad":
while_grad_sub_block_ids.append(op.attr("sub_block").id)
# Find while/while_grad block pair
for grad_id in while_grad_sub_block_ids:
parent_id = pdesc.block(grad_id).parent
if parent_id in while_sub_block_ids:
while_pair.append((parent_id, grad_id))
while_sub_block_ids.remove(parent_id)
# Get while/while_grad block ops
for parent_id, grad_id in while_pair:
while_block_ops = []
while_block = pdesc.block(parent_id)
while_block_op_size = while_block.op_size()
for i in range(while_block_op_size):
while_block_ops.append(while_block.op(i))
while_grad_block = pdesc.block(grad_id)
while_grad_block_op_size = while_grad_block.op_size()
for i in range(while_grad_block_op_size):
while_block_ops.append(while_grad_block.op(i))
ops_list.append((while_block_ops, while_block_op_size))
# Process rest while block ops
for parent_id in while_sub_block_ids:
while_block_ops = []
while_block = pdesc.block(parent_id)
while_block_op_size = while_block.op_size()
for i in range(while_block_op_size):
while_block_ops.append(while_block.op(i))
ops_list.append((while_block_ops, while_block_op_size))
cfgs = [ControlFlowGraph(input_program, i, j) for i, j in ops_list]
return cfgs
def memory_optimize(input_program):
graph = ControlFlowGraph(input_program)
graph.memory_optimize()
result_program = graph.get_program()
return result_program
cfgs = get_cfgs(input_program)
for cfg in cfgs:
cfg.memory_optimize()
......@@ -16,6 +16,11 @@ import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid as fluid
# need to fix random seed and training data to compare the loss
# value accurately calculated by the default and the memory optimization
# version.
fluid.default_startup_program().random_seed = 111
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
......@@ -28,15 +33,18 @@ avg_cost = fluid.layers.mean(x=cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.1)
sgd_optimizer.minimize(avg_cost)
# memopt_program = fluid.default_main_program()
memopt_program = fluid.memory_optimize(fluid.default_main_program())
fluid.memory_optimize(fluid.default_main_program())
BATCH_SIZE = 200
# fix the order of training data
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.uci_housing.train(), buf_size=500),
batch_size=BATCH_SIZE)
paddle.dataset.uci_housing.train(), batch_size=BATCH_SIZE)
# train_reader = paddle.batch(
# paddle.reader.shuffle(
# paddle.dataset.uci_housing.train(), buf_size=500),
# batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
......@@ -49,7 +57,7 @@ for pass_id in range(PASS_NUM):
fluid.io.save_persistables(exe, "./fit_a_line.model/")
fluid.io.load_persistables(exe, "./fit_a_line.model/")
for data in train_reader():
avg_loss_value, = exe.run(memopt_program,
avg_loss_value, = exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[avg_cost])
......
......@@ -19,6 +19,11 @@ import sys
import paddle.v2 as paddle
import paddle.v2.fluid as fluid
# need to fix random seed and training data to compare the loss
# value accurately calculated by the default and the memory optimization
# version.
fluid.default_startup_program().random_seed = 111
def resnet_cifar10(input, depth=32):
def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu'):
......@@ -117,31 +122,37 @@ opts = optimizer.minimize(avg_cost)
accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
# memopt_program = fluid.default_main_program()
memopt_program = fluid.memory_optimize(fluid.default_main_program())
fluid.memory_optimize(fluid.default_main_program())
BATCH_SIZE = 128
PASS_NUM = 1
# fix the order of training data
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.cifar.train10(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
paddle.dataset.cifar.train10(), batch_size=BATCH_SIZE)
# train_reader = paddle.batch(
# paddle.reader.shuffle(
# paddle.dataset.cifar.train10(), buf_size=128 * 10),
# batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
exe.run(fluid.default_startup_program())
i = 0
for pass_id in range(PASS_NUM):
accuracy.reset(exe)
for data in train_reader():
loss, acc = exe.run(memopt_program,
loss, acc = exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[avg_cost] + accuracy.metrics)
pass_acc = accuracy.eval(exe)
print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
pass_acc))
# this model is slow, so if we can train two mini batch, we think it works properly.
exit(0)
if i > 2:
exit(0)
i += 1
exit(1)
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid as fluid
import paddle.v2.fluid.core as core
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.executor import Executor
dict_size = 30000
source_dict_dim = target_dict_dim = dict_size
src_dict, trg_dict = paddle.dataset.wmt14.get_dict(dict_size)
hidden_dim = 32
word_dim = 16
IS_SPARSE = True
batch_size = 10
max_length = 50
topk_size = 50
trg_dic_size = 10000
decoder_size = hidden_dim
# need to fix random seed and training data to compare the loss
# value accurately calculated by the default and the memory optimization
# version.
fluid.default_startup_program().random_seed = 111
def encoder_decoder():
# encoder
src_word_id = layers.data(
name="src_word_id", shape=[1], dtype='int64', lod_level=1)
src_embedding = layers.embedding(
input=src_word_id,
size=[dict_size, word_dim],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr=fluid.ParamAttr(name='vemb'))
fc1 = fluid.layers.fc(input=src_embedding, size=hidden_dim * 4, act='tanh')
lstm_hidden0, lstm_0 = layers.dynamic_lstm(input=fc1, size=hidden_dim * 4)
encoder_out = layers.sequence_last_step(input=lstm_hidden0)
# decoder
trg_language_word = layers.data(
name="target_language_word", shape=[1], dtype='int64', lod_level=1)
trg_embedding = layers.embedding(
input=trg_language_word,
size=[dict_size, word_dim],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr=fluid.ParamAttr(name='vemb'))
rnn = fluid.layers.DynamicRNN()
with rnn.block():
current_word = rnn.step_input(trg_embedding)
mem = rnn.memory(init=encoder_out)
fc1 = fluid.layers.fc(input=[current_word, mem],
size=decoder_size,
act='tanh')
out = fluid.layers.fc(input=fc1, size=target_dict_dim, act='softmax')
rnn.update_memory(mem, fc1)
rnn.output(out)
return rnn()
def to_lodtensor(data, place):
seq_lens = [len(seq) for seq in data]
cur_len = 0
lod = [cur_len]
for l in seq_lens:
cur_len += l
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = core.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
def main():
rnn_out = encoder_decoder()
label = layers.data(
name="target_language_next_word", shape=[1], dtype='int64', lod_level=1)
cost = layers.cross_entropy(input=rnn_out, label=label)
avg_cost = fluid.layers.mean(x=cost)
optimizer = fluid.optimizer.Adagrad(learning_rate=1e-4)
optimizer.minimize(avg_cost)
fluid.memory_optimize(fluid.default_main_program())
# fix the order of training data
train_data = paddle.batch(
paddle.dataset.wmt14.train(dict_size), batch_size=batch_size)
# train_data = paddle.batch(
# paddle.reader.shuffle(
# paddle.dataset.wmt14.train(dict_size), buf_size=1000),
# batch_size=batch_size)
place = core.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
batch_id = 0
for pass_id in xrange(10):
for data in train_data():
word_data = to_lodtensor(map(lambda x: x[0], data), place)
trg_word = to_lodtensor(map(lambda x: x[1], data), place)
trg_word_next = to_lodtensor(map(lambda x: x[2], data), place)
outs = exe.run(fluid.default_main_program(),
feed={
'src_word_id': word_data,
'target_language_word': trg_word,
'target_language_next_word': trg_word_next
},
fetch_list=[avg_cost])
avg_cost_val = np.array(outs[0])
print('pass_id=' + str(pass_id) + ' batch=' + str(batch_id) +
" avg_cost=" + str(avg_cost_val))
if batch_id > 2:
exit(0)
batch_id += 1
if __name__ == '__main__':
main()
......@@ -68,4 +68,6 @@ class TestUnpoolOp(OpTest):
if __name__ == '__main__':
unittest.main()
# FIXME: detection_output_op will be rewritten. This unittest should be
# enabled after rewriting.
exit(0) # temporary disable this unittest
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import sys
import math
from op_test import OpTest
class TestIOUSimilarityOp(OpTest):
def test_check_output(self):
self.check_output()
def setUp(self):
self.op_type = "iou_similarity"
self.boxes1 = np.array(
[[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]]).astype('float32')
self.boxes2 = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
[0.0, 0.0, 20.0, 20.0]]).astype('float32')
self.output = np.array(
[[2.0 / 16.0, 0, 6.0 / 400.0],
[1.0 / 16.0, 0.0, 5.0 / 400.0]]).astype('float32')
self.inputs = {'X': self.boxes1, 'Y': self.boxes2}
self.outputs = {'Out': self.output}
class TestIOUSimilarityOpWithLoD(TestIOUSimilarityOp):
def test_check_output(self):
self.check_output()
def setUp(self):
super(TestIOUSimilarityOpWithLoD, self).setUp()
self.boxes1_lod = [[0, 1, 2]]
self.output_lod = [[0, 1, 2]]
self.inputs = {'X': (self.boxes1, self.boxes1_lod), 'Y': self.boxes2}
self.outputs = {'Out': (self.output, self.output_lod)}
if __name__ == '__main__':
unittest.main()
......@@ -17,8 +17,9 @@ import unittest
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.nets as nets
from paddle.v2.fluid.framework import Program, program_guard
from paddle.v2.fluid.framework import Program, program_guard, default_main_program
from paddle.v2.fluid.param_attr import ParamAttr
import decorators
class TestBook(unittest.TestCase):
......@@ -225,6 +226,51 @@ class TestBook(unittest.TestCase):
self.assertIsNotNone(out)
print(str(program))
def test_im2sequence(self):
print("test_im2sequence")
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[3, 128, 128], dtype='float32')
output = layers.im2sequence(
input=x, stride=[1, 1], filter_size=[2, 2])
self.assertIsNotNone(output)
print(str(program))
@decorators.prog_scope()
def test_nce(self):
window_size = 5
words = []
for i in xrange(window_size):
words.append(
layers.data(
name='word_{0}'.format(i), shape=[1], dtype='int64'))
dict_size = 10000
label_word = int(window_size / 2) + 1
embs = []
for i in xrange(window_size):
if i == label_word:
continue
emb = layers.embedding(
input=words[i],
size=[dict_size, 32],
param_attr='emb.w',
is_sparse=True)
embs.append(emb)
embs = layers.concat(input=embs, axis=1)
loss = layers.nce(input=embs,
label=words[label_word],
num_total_classes=dict_size,
param_attr='nce.w',
bias_attr='nce.b')
avg_loss = layers.mean(x=loss)
self.assertIsNotNone(avg_loss)
print(str(default_main_program()))
if __name__ == '__main__':
unittest.main()
......@@ -109,4 +109,6 @@ class TestNCECase1(TestNCE):
if __name__ == '__main__':
# FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/7778
exit(0)
unittest.main()
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import sys
import math
from op_test import OpTest
class TestPriorBoxOp(OpTest):
def set_data(self):
self.init_test_params()
self.init_test_input()
self.init_test_output()
self.inputs = {'Input': self.input, 'Image': self.image}
self.attrs = {
'min_sizes': self.min_sizes,
'max_sizes': self.max_sizes,
'aspect_ratios': self.aspect_ratios,
'variances': self.variances,
'flip': self.flip,
'clip': self.clip,
'step_w': self.step_w,
'step_h': self.step_h,
'offset': self.offset
}
self.outputs = {'Boxes': self.out_boxes, 'Variances': self.out_var}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
return
def setUp(self):
self.op_type = "prior_box"
self.set_data()
def init_test_params(self):
self.layer_w = 4
self.layer_h = 4
self.image_w = 20
self.image_h = 20
self.step_w = float(self.image_w) / float(self.layer_w)
self.step_h = float(self.image_h) / float(self.layer_h)
self.input_channels = 2
self.image_channels = 3
self.batch_size = 10
self.min_sizes = [2, 4]
self.min_sizes = np.array(self.min_sizes).astype('int64')
self.max_sizes = [5, 10]
self.max_sizes = np.array(self.max_sizes).astype('int64')
self.aspect_ratios = [2.0, 3.0]
self.flip = True
self.real_aspect_ratios = [1, 2.0, 1.0 / 2.0, 3.0, 1.0 / 3.0]
self.aspect_ratios = np.array(
self.aspect_ratios, dtype=np.float).flatten()
self.variances = [0.1, 0.1, 0.2, 0.2]
self.variances = np.array(self.variances, dtype=np.float).flatten()
self.clip = True
self.num_priors = len(self.real_aspect_ratios) * len(self.min_sizes)
if len(self.max_sizes) > 1:
self.num_priors += len(self.max_sizes)
self.offset = 0.5
def init_test_input(self):
self.image = np.random.random(
(self.batch_size, self.image_channels, self.image_w,
self.image_h)).astype('float32')
self.input = np.random.random(
(self.batch_size, self.input_channels, self.layer_w,
self.layer_h)).astype('float32')
def init_test_output(self):
out_dim = (self.layer_h, self.layer_w, self.num_priors, 4)
out_boxes = np.zeros(out_dim).astype('float32')
out_var = np.zeros(out_dim).astype('float32')
idx = 0
for h in range(self.layer_h):
for w in range(self.layer_w):
c_x = (w + self.offset) * self.step_w
c_y = (h + self.offset) * self.step_h
idx = 0
for s in range(len(self.min_sizes)):
min_size = self.min_sizes[s]
c_w = c_h = min_size / 2.
out_boxes[h, w, idx, :] = [
(c_x - c_w) / self.image_w, (c_y - c_h) / self.image_h,
(c_x + c_w) / self.image_w, (c_y + c_h) / self.image_h
]
idx += 1
if len(self.max_sizes) > 0:
max_size = self.max_sizes[s]
# second prior: aspect_ratio = 1,
c_w = c_h = math.sqrt(min_size * max_size) / 2
out_boxes[h, w, idx, :] = [(c_x - c_w) / self.image_w,
(c_y - c_h) / self.image_h,
(c_x + c_w) / self.image_w,
(c_y + c_h) / self.image_h]
idx += 1
# rest of priors
for r in range(len(self.real_aspect_ratios)):
ar = self.real_aspect_ratios[r]
if math.fabs(ar - 1.) < 1e-6:
continue
c_w = min_size * math.sqrt(ar) / 2
c_h = (min_size / math.sqrt(ar)) / 2
out_boxes[h, w, idx, :] = [(c_x - c_w) / self.image_w,
(c_y - c_h) / self.image_h,
(c_x + c_w) / self.image_w,
(c_y + c_h) / self.image_h]
idx += 1
# clip the prior's coordidate such that it is within[0, 1]
if self.clip:
out_boxes = np.clip(out_boxes, 0.0, 1.0)
# set the variance.
out_var = np.tile(self.variances, (self.layer_h, self.layer_w,
self.num_priors, 1))
self.out_boxes = out_boxes.astype('float32')
self.out_var = out_var.astype('float32')
if __name__ == '__main__':
unittest.main()
......@@ -321,11 +321,12 @@ def simple_transform(im,
"""
im = resize_short(im, resize_size)
if is_train:
im = random_crop(im, crop_size, is_color)
im = random_crop(im, crop_size, is_color=is_color)
if np.random.randint(2) == 0:
im = left_right_flip(im, is_color)
else:
im = center_crop(im, crop_size, is_color)
im = center_crop(im, crop_size, is_color=is_color)
if len(im.shape) == 3:
im = to_chw(im)
......
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册