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PaddleDetection

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PaddleDetection
提交 ce4e0e90 编写于 作者: W wanghaoshuang
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix_crop

上级 67a3277e 65c859db
隐藏空白更改
内联 并排
Showing with 474 addition and 309 deletion
paddle/framework/backward.cc
浏览文件 @ ce4e0e90
......@@ -522,7 +522,7 @@ ParamGradInfoMap AppendBackward(
new OpDescBind("fill_constant", {}, {{"Out", {fill_one_op_out}}},
{{"shape", std::vector<int>{1}},
{"value", static_cast<float>(1.0)},
{"data_type", target.GetDataType()}}));
{"dtype", target.GetDataType()}}));
// infer var type of fill_one_op
fill_one_op->InferVarType(root_block);
......
paddle/framework/executor.cc
浏览文件 @ ce4e0e90
......@@ -120,7 +120,7 @@ void Executor::Run(const ProgramDescBind& pdesc, Scope* scope, int block_id,
for (auto& op_desc : block.AllOps()) {
auto op = paddle::framework::OpRegistry::CreateOp(*op_desc);
VLOG(10) << op->DebugString();
VLOG(3) << op->DebugString();
op->Run(*local_scope, *device);
}
if (create_local_scope) {
......
paddle/framework/prune.cc
浏览文件 @ ce4e0e90
......@@ -26,6 +26,8 @@ namespace framework {
const std::string kFeedOpType = "feed";
const std::string kFetchOpType = "fetch";
const std::string kDropOutOpType = "dropout";
const std::string kBatchNormOpType = "batch_norm";
bool HasDependentVar(const OpDesc& op_desc,
const std::set<std::string>& dependent_vars) {
......@@ -106,5 +108,26 @@ void Prune(const ProgramDesc& input, ProgramDesc* output) {
prune_impl(input, output, 0);
}
void inference_optimize_impl(const ProgramDesc& input, ProgramDesc* output,
int block_id) {
*output = input;
auto* op_field = output->mutable_blocks(block_id)->mutable_ops();
for (auto& op_desc : *op_field) {
if (op_desc.type() == kDropOutOpType ||
op_desc.type() == kBatchNormOpType) {
for (auto& attr : *op_desc.mutable_attrs()) {
if (attr.name() == "is_test") {
attr.set_b(true);
break;
}
}
}
}
}
void InferenceOptimize(const ProgramDesc& input, ProgramDesc* output) {
inference_optimize_impl(input, output, 0);
}
} // namespace framework
} // namespace paddle
paddle/framework/prune.h
浏览文件 @ ce4e0e90
......@@ -22,5 +22,7 @@ namespace framework {
void Prune(const ProgramDesc& input, ProgramDesc* output);
void InferenceOptimize(const ProgramDesc& input, ProgramDesc* output);
} // namespace framework
} // namespace paddle
paddle/framework/tensor_array.cc
浏览文件 @ ce4e0e90
......@@ -302,7 +302,7 @@ LoDTensor TensorArray::Stack() const {
const auto& first_dims = values_.front().dims();
// check all the values have the same shape
// TODO(superjom) check the same dtypes
// TODO(superjom) check the same data_type
for (size_t idx = 1; idx < size(); idx++) {
const auto& value_dims = values_[idx].dims();
PADDLE_ENFORCE_EQ(first_dims, value_dims);
......
paddle/memory/CMakeLists.txt
浏览文件 @ ce4e0e90
add_subdirectory(detail)
cc_library(memory SRCS memory.cc DEPS place)
cc_library(memory SRCS memory.cc DEPS place enforce)
cc_library(memcpy SRCS memcpy.cc)
cc_library(paddle_memory
......
paddle/operators/beam_search_decode_op.cc
浏览文件 @ ce4e0e90
......@@ -17,6 +17,36 @@ limitations under the License. */
namespace paddle {
namespace operators {
struct BeamSearchDecodeFunctor {
BeamSearchDecodeFunctor(const LoDTensorArray& step_ids,
const LoDTensorArray& step_scores,
LoDTensor* id_tensor, LoDTensor* score_tensor)
: step_ids_(step_ids),
step_scores_(step_scores),
id_tensor_(id_tensor),
score_tensor_(score_tensor) {}
template <typename T>
void operator()() const;
const LoDTensorArray& step_ids_;
const LoDTensorArray& step_scores_;
LoDTensor* id_tensor_;
LoDTensor* score_tensor_;
};
template <typename T>
void BeamSearchDecodeFunctor::operator()() const {
BeamSearchDecoder<T> beam_search_decoder;
beam_search_decoder.PackAllSteps(step_ids_, step_scores_, id_tensor_,
score_tensor_);
}
template <>
void BeamSearchDecodeFunctor::operator()<bool>() const {
PADDLE_THROW("beam search decode op does not support bool!");
}
class BeamSearchDecodeOp : public framework::OperatorBase {
public:
BeamSearchDecodeOp(const std::string& type,
......@@ -45,9 +75,9 @@ class BeamSearchDecodeOp : public framework::OperatorBase {
LoDTensor* sentenceIds = ctx.Output<LoDTensor>("SentenceIds");
LoDTensor* sentenceScores = ctx.Output<LoDTensor>("SentenceScores");
BeamSearchDecoder<float> beam_search_decoder;
beam_search_decoder.PackAllSteps(*ids, *scores, sentenceIds,
sentenceScores);
framework::VisitDataType(
framework::ToDataType(scores->at(0).type()),
BeamSearchDecodeFunctor(*ids, *scores, sentenceIds, sentenceScores));
}
};
......
paddle/operators/bilinear_tensor_product_op.cc
浏览文件 @ ce4e0e90
......@@ -77,11 +77,19 @@ class BilinearTensorProductOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("Out", "The output of bilinear_tensor_product operator.");
AddComment(R"DOC(
Bilinear Tensor Product operator.
Given input X and Y, a 3D tensor weight, and bias. Each column of the
output is computed by one slice i = 1, . . . , k of the tensor:
M = (X W_i) \cdot Y
Out_i = \sum_i {M_i} + Bias_i
Given input X and Y, a 3D tensor Weight and a Bias. Each column of the
Output is computed by one slice $i = 1, . . . , k$ of the tensor:
$$
M = (X W_i) * Y \\
Out_i = \sum_j {M_j} + Bias_i
$$
Where $W_i$ is the $i$-th slice of Input(Weight);
$M_j$ is the $j$-th column of $M$;
$Out_i$ is the $i$-th column of Output(Out);
$Bias_i$ is a column vector, each element of it is equal to
the $i$-th element of $Bias$;
)DOC");
}
......
paddle/operators/cast_op.cc
浏览文件 @ ce4e0e90
......@@ -25,8 +25,8 @@ class CastOpProtoMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input tensor of cast op");
AddOutput("Out", "The output tensor of cast op");
AddAttr<int>("out_data_type", "output data type");
AddAttr<int>("in_data_type", "input data type");
AddAttr<int>("out_dtype", "output data type");
AddAttr<int>("in_dtype", "input data type");
AddComment(R"DOC(
Cast Operator.
......@@ -58,8 +58,8 @@ class CastOpGradMaker : public framework::SingleGradOpDescMaker {
grad->SetType("cast");
grad->SetInput("X", OutputGrad("Out"));
grad->SetOutput("Out", InputGrad("X"));
grad->SetAttr("out_data_type", GetAttr("in_data_type"));
grad->SetAttr("in_data_type", GetAttr("out_data_type"));
grad->SetAttr("out_dtype", GetAttr("in_dtype"));
grad->SetAttr("in_dtype", GetAttr("out_dtype"));
return std::unique_ptr<framework::OpDescBind>(grad);
}
};
......
paddle/operators/cast_op.h
浏览文件 @ ce4e0e90
......@@ -55,7 +55,7 @@ class CastOpKernel : public framework::OpKernel<InT> {
auto* in = context.Input<framework::Tensor>("X");
auto* out = context.Output<framework::Tensor>("Out");
framework::VisitDataType(
static_cast<framework::DataType>(context.Attr<int>("out_data_type")),
static_cast<framework::DataType>(context.Attr<int>("out_dtype")),
CastOpFunctor<Place, InT>(in, out, context.device_context()));
}
};
......
paddle/operators/dropout_op.cc
浏览文件 @ ce4e0e90
......@@ -30,7 +30,7 @@ class DropoutOp : public framework::OperatorWithKernel {
auto x_dims = ctx->GetInputDim("X");
ctx->SetOutputDim("Out", x_dims);
if (ctx->Attrs().Get<bool>("is_training") == true) {
if (ctx->Attrs().Get<bool>("is_test") == false) {
ctx->SetOutputDim("Mask", x_dims);
}
ctx->ShareLoD("X", /*->*/ "Out");
......@@ -49,7 +49,7 @@ class DropoutOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<float>("dropout_prob", "Probability of setting units to zero.")
.SetDefault(.5f);
AddAttr<bool>("is_training", "True if in training phase.").SetDefault(true);
AddAttr<bool>("is_test", "True if in test phase.").SetDefault(false);
AddAttr<int>("seed", "Dropout random seed.").SetDefault(0);
AddComment(R"DOC(
......@@ -71,8 +71,8 @@ class DropoutOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE_EQ(ctx->Attrs().Get<bool>("is_training"), true,
"GradOp is only callable when is_training is true");
PADDLE_ENFORCE_EQ(ctx->Attrs().Get<bool>("is_test"), false,
"GradOp is only callable when is_test is false");
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
PADDLE_ENFORCE(ctx->HasInput("Mask"), "Mask must not be null.");
......
paddle/operators/dropout_op.cu
浏览文件 @ ce4e0e90
......@@ -59,7 +59,7 @@ class GPUDropoutKernel : public framework::OpKernel<T> {
auto Y = EigenMatrix<T>::Reshape(*y, 1);
auto place = context.GetEigenDevice<Place>();
if (context.Attr<bool>("is_training")) {
if (!context.Attr<bool>("is_test")) {
auto* mask = context.Output<Tensor>("Mask");
auto* mask_data = mask->mutable_data<T>(context.GetPlace());
int size = framework::product(mask->dims());
......
paddle/operators/dropout_op.h
浏览文件 @ ce4e0e90
......@@ -35,7 +35,7 @@ class CPUDropoutKernel : public framework::OpKernel<T> {
auto* y_data = y->mutable_data<T>(context.GetPlace());
float dropout_prob = context.Attr<float>("dropout_prob");
if (context.Attr<bool>("is_training")) {
if (!context.Attr<bool>("is_test")) {
auto* mask = context.Output<Tensor>("Mask");
auto* mask_data = mask->mutable_data<T>(context.GetPlace());
int seed = context.Attr<int>("seed");
......@@ -65,8 +65,8 @@ template <typename Place, typename T>
class DropoutGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
PADDLE_ENFORCE(context.Attr<bool>("is_training"),
"GradOp is only callable when is_training is true");
PADDLE_ENFORCE(!context.Attr<bool>("is_test"),
"GradOp is only callable when is_test is false");
auto* grad_x = context.Output<Tensor>(framework::GradVarName("X"));
auto* grad_y = context.Input<Tensor>(framework::GradVarName("Out"));
......
paddle/operators/fill_constant_batch_size_like_op.cc
浏览文件 @ ce4e0e90
......@@ -52,7 +52,7 @@ class FillConstantBatchSizeLikeOp : public framework::OperatorWithKernel {
framework::OpKernelType GetKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
static_cast<framework::DataType>(ctx.Attr<int>("data_type")),
static_cast<framework::DataType>(ctx.Attr<int>("dtype")),
ctx.device_context());
}
};
......@@ -63,7 +63,7 @@ class FillConstantBatchSizeLikeOpMaker
FillConstantBatchSizeLikeOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddAttr<int>("data_type",
AddAttr<int>("dtype",
"(int, default 5 (FP32)) "
"Output data type")
.SetDefault(framework::DataType::FP32);
......
paddle/operators/fill_constant_op.cc
浏览文件 @ ce4e0e90
......@@ -34,7 +34,7 @@ class FillConstantOp : public framework::OperatorBase {
using framework::OperatorBase::OperatorBase;
void Run(const framework::Scope &scope,
const platform::DeviceContext &dev_ctx) const override {
auto data_type = static_cast<framework::DataType>(Attr<int>("data_type"));
auto data_type = static_cast<framework::DataType>(Attr<int>("dtype"));
auto value = Attr<float>("value");
auto force_cpu = Attr<bool>("force_cpu");
auto &out =
......@@ -55,7 +55,7 @@ class FillConstantOpMaker : public framework::OpProtoAndCheckerMaker {
FillConstantOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddAttr<int>("data_type",
AddAttr<int>("dtype",
"(int, default 5 (FP32)) "
"Output data type")
.SetDefault(framework::DataType::FP32);
......
paddle/operators/gaussian_random_op.cc
浏览文件 @ ce4e0e90
......@@ -60,7 +60,7 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
framework::OpKernelType GetKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
static_cast<framework::DataType>(ctx.Attr<int>("data_type")),
static_cast<framework::DataType>(ctx.Attr<int>("dtype")),
ctx.device_context());
}
};
......@@ -88,7 +88,7 @@ class GaussianRandomOpMaker : public framework::OpProtoAndCheckerMaker {
"Random seed of generator."
"0 means use system wide seed.")
.SetDefault(0);
AddAttr<int>("data_type",
AddAttr<int>("dtype",
"(int, default 5(FP32)) "
"Output data type.")
.SetDefault(framework::DataType::FP32);
......
paddle/operators/linear_chain_crf_op.cc
浏览文件 @ ce4e0e90
......@@ -32,19 +32,19 @@ class LinearChainCRFOpMaker : public framework::OpProtoAndCheckerMaker {
"[(D + 2) x D]. The learnable parameter for the linear_chain_crf "
"operator. See more details in the operator's comments.");
AddInput("Label",
"(LoDTensor, default LoDTensor<int>) A LoDTensor with shape "
"(LoDTensor, default LoDTensor<int64_t>) A LoDTensor with shape "
"[N x 1], where N is the total element number in a mini-batch. "
"The ground truth.");
AddOutput(
"Alpha",
"(Tensor, default Tensor<float>) A 2-D Tensor with shape [N x D]. "
"The forward vectors for the entire batch. Denote it as \f$\alpha\f$. "
"\f$\alpha$\f is a memo table used to calculate the normalization "
"factor in CRF. \f$\alpha[k, v]$\f stores the unnormalized "
"The forward vectors for the entire batch. Denote it as $\alpha$. "
"$\alpha$ is a memo table used to calculate the normalization "
"factor in CRF. $\alpha[k, v]$ stores the unnormalized "
"probabilites of all possible unfinished sequences of tags that end at "
"position \f$k$\f with tag \f$v$\f. For each \f$k$\f, "
"\f$\alpha[k, v]$\f is a vector of length \f$D$\f with a component for "
"each tag value \f$v$\f. This vector is called a forward vecotr and "
"position $k$ with tag $v$. For each $k$, "
"$\alpha[k, v]$ is a vector of length $D$ with a component for "
"each tag value $v$. This vector is called a forward vecotr and "
"will also be used in backward computations.")
.AsIntermediate();
AddOutput(
......@@ -73,9 +73,9 @@ LinearChainCRF Operator.
Conditional Random Field defines an undirected probabilistic graph with nodes
denoting random variables and edges denoting dependencies between these
variables. CRF learns the conditional probability \f$P(Y|X)\f$, where
\f$X = (x_1, x_2, ... , x_n)\f$ are structured inputs and
\f$Y = (y_1, y_2, ... , y_n)\f$ are labels for the inputs.
variables. CRF learns the conditional probability $P(Y|X)$, where
$X = (x_1, x_2, ... , x_n)$ are structured inputs and
$Y = (y_1, y_2, ... , y_n)$ are labels for the inputs.
Linear chain CRF is a special case of CRF that is useful for sequence labeling
task. Sequence labeling tasks do not assume a lot of conditional
......@@ -88,21 +88,22 @@ CRF. Please refer to http://www.cs.columbia.edu/~mcollins/fb.pdf and
http://cseweb.ucsd.edu/~elkan/250Bwinter2012/loglinearCRFs.pdf for details.
Equation:
1. Denote Input(Emission) to this operator as \f$x\f$ here.
1. Denote Input(Emission) to this operator as $x$ here.
2. The first D values of Input(Transition) to this operator are for starting
weights, denoted as \f$a\f$ here.
weights, denoted as $a$ here.
3. The next D values of Input(Transition) of this operator are for ending
weights, denoted as \f$b\f$ here.
weights, denoted as $b$ here.
4. The remaning values of Input(Transition) are for transition weights,
denoted as \f$w\f$ here.
5. Denote Input(Label) as \f$s\f$ here.
The probability of a sequence \f$s\f$ of length \f$L\f$ is defined as:
\f$P(s) = (1/Z) \exp(a_{s_1} + b_{s_L}
+ \sum_{l=1}^L x_{s_l}
+ \sum_{l=2}^L w_{s_{l-1},s_l})\f$
where \f$Z\f$ is a normalization value so that the sum of \f$P(s)\f$ over
all possible sequences is \f$1\f$, and \f$x\f$ is the emission feature weight
denoted as $w$ here.
5. Denote Input(Label) as $s$ here.
The probability of a sequence $s$ of length $L$ is defined as:
$$P(s) = (1/Z) \exp(a_{s_1} + b_{s_L}
+ \sum_{l=1}^L x_{s_l}
+ \sum_{l=2}^L w_{s_{l-1},s_l})$$
where $Z$ is a normalization value so that the sum of $P(s)$ over
all possible sequences is 1, and $x$ is the emission feature weight
to the linear chain CRF.
Finally, the linear chain CRF operator outputs the logarithm of the conditional
......
paddle/operators/nccl_op.cc
浏览文件 @ ce4e0e90
......@@ -49,7 +49,7 @@ class NCCLInitOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("Communicator",
"Create Communicator for communicating between gpus");
AddAttr<std::vector<int>>("gpus", "(vector<int>) GPU id lists");
AddAttr<int>("data_type",
AddAttr<int>("dtype",
"(int, default 5 (FP32)) "
"Output data type")
.SetDefault(framework::DataType::FP32);
......
paddle/operators/recurrent_op.cc
浏览文件 @ ce4e0e90
......@@ -401,7 +401,7 @@ class RecurrentGradOp : public RecurrentBase {
auto &inside_tensor = cur_scope.FindVar(inside_grad_name)
->Get<framework::LoDTensor>();
framework::AttributeMap attrs;
attrs["data_type"] = framework::ToDataType(inside_tensor.type());
attrs["dtype"] = framework::ToDataType(inside_tensor.type());
attrs["shape"] = framework::vectorize2int(inside_tensor.dims());
attrs["value"] = 0.0f;
......
paddle/operators/rnn_memory_helper_op.cc
浏览文件 @ ce4e0e90
......@@ -62,7 +62,7 @@ class RNNMemoryHelperOpInfoMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "");
AddOutput("Out", "");
AddAttr<int>("data_type",
AddAttr<int>("dtype",
"(int, default 5 (FP32)) "
"Output data type")
.SetDefault(framework::DataType::FP32);
......@@ -95,7 +95,7 @@ class RNNMemoryHelperGradOp : public framework::OperatorBase {
auto &in_var_tensor = in_var->Get<framework::LoDTensor>();
framework::AttributeMap attrs;
attrs["data_type"] = framework::ToDataType(in_var_tensor.type());
attrs["dtype"] = framework::ToDataType(in_var_tensor.type());
attrs["shape"] = framework::vectorize2int(in_var_tensor.dims());
attrs["value"] = 0.0f;
......@@ -121,7 +121,7 @@ class RNNMemoryHelperGradOpInfoMaker
AddInput("X", "");
AddInput("Out", "");
AddOutput(framework::GradVarName("X"), "");
AddAttr<int>("data_type",
AddAttr<int>("dtype",
"(int, default 5 (FP32)) "
"Output data type")
.SetDefault(framework::DataType::FP32);
......
paddle/operators/softmax_op.cc
浏览文件 @ ce4e0e90
......@@ -59,7 +59,7 @@ Then the ratio of the exponential of the given dimension and the sum of
exponential values of all the other dimensions is the output of the softmax
operator.
For each row `i` and each column `j` in input X, we have:
For each row $i$ and each column $j$ in Input(X), we have:
$$Y[i, j] = \frac{\exp(X[i, j])}{\sum_j(exp(X[i, j])}$$
)DOC");
......
paddle/operators/softmax_with_cross_entropy_op.cc
浏览文件 @ ce4e0e90
......@@ -67,15 +67,15 @@ The equation is as follows:
1) Hard label (one-hot label, so every sample has exactly one class)
$$Loss_j = \f$ -\text{Logit}_{Label_j} +
$$Loss_j = -\text{Logit}_{Label_j} +
\log\left(\sum_{i=0}^{K}\exp(\text{Logit}_i)\right),
j = 1, ..., K $\f$$
j = 1,..., K$$
2) Soft label (each sample can have a distribution over all classes)
$$Loss_j = \f$ -\sum_{i=0}^{K}\text{Label}_i\left(\text{Logit}_i -
$$Loss_j = -\sum_{i=0}^{K}\text{Label}_i \left(\text{Logit}_i -
\log\left(\sum_{i=0}^{K}\exp(\text{Logit}_i)\right)\right),
j = 1,...,K $\f$$
j = 1,...,K$$
)DOC");
}
......
paddle/operators/uniform_random_op.cc
浏览文件 @ ce4e0e90
......@@ -66,7 +66,7 @@ class UniformRandomOp : public framework::OperatorWithKernel {
framework::OpKernelType GetKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
static_cast<framework::DataType>(ctx.Attr<int>("data_type")),
static_cast<framework::DataType>(ctx.Attr<int>("dtype")),
ctx.device_context());
}
};
......@@ -99,7 +99,7 @@ uniform distribution.
"Random seed used for generating samples. "
"0 means use a seed generated by the system.")
.SetDefault(0);
AddAttr<int>("data_type", "(int, default 5(FP32)) Output tensor data type")
AddAttr<int>("dtype", "(int, default 5(FP32)) Output tensor data type")
.SetDefault(framework::DataType::FP32);
}
};
......
paddle/operators/while_op.cc
浏览文件 @ ce4e0e90
......@@ -180,7 +180,7 @@ class WhileGradOp : public framework::OperatorBase {
if (var->IsType<LoDTensor>()) {
auto &inside_tensor = var->Get<framework::LoDTensor>();
framework::AttributeMap attrs;
attrs["data_type"] = framework::ToDataType(inside_tensor.type());
attrs["dtype"] = framework::ToDataType(inside_tensor.type());
attrs["shape"] = framework::vectorize2int(inside_tensor.dims());
attrs["value"] = 0.0f;
......
paddle/platform/CMakeLists.txt
浏览文件 @ ce4e0e90
cc_library(cpu_info SRCS cpu_info.cc DEPS gflags glog)
if(WITH_GPU)
cc_library(enforce SRCS enforce.cc DEPS nccl)
else()
cc_library(enforce SRCS enforce.cc)
endif()
cc_test(enforce_test SRCS enforce_test.cc DEPS stringpiece enforce)
cc_library(cpu_info SRCS cpu_info.cc DEPS gflags glog enforce)
cc_test(cpu_info_test SRCS cpu_info_test.cc DEPS cpu_info)
nv_library(gpu_info SRCS gpu_info.cc DEPS gflags glog)
nv_library(gpu_info SRCS gpu_info.cc DEPS gflags glog enforce)
cc_library(place SRCS place.cc)
cc_library(place SRCS place.cc DEPS enforce)
cc_test(place_test SRCS place_test.cc DEPS place glog gflags)
add_subdirectory(dynload)
cc_test(enforce_test SRCS enforce_test.cc DEPS stringpiece)
IF(WITH_GPU)
set(GPU_CTX_DEPS dynload_cuda dynamic_loader)
ELSE()
......
paddle/platform/dynload/CMakeLists.txt
浏览文件 @ ce4e0e90
cc_library(dynamic_loader SRCS dynamic_loader.cc DEPS glog gflags)
cc_library(dynamic_loader SRCS dynamic_loader.cc DEPS glog gflags enforce)
nv_library(dynload_cuda SRCS cublas.cc cudnn.cc curand.cc nccl.cc
DEPS dynamic_loader nccl)
paddle/platform/enforce.cc 0 → 100644
浏览文件 @ ce4e0e90
/* 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/platform/enforce.h"
namespace paddle {
namespace platform {} // namespace platform
} // namespace paddle
paddle/platform/enforce.h
浏览文件 @ ce4e0e90
......@@ -49,7 +49,6 @@ limitations under the License. */
namespace paddle {
namespace platform {
namespace {
#ifdef __GNUC__
inline std::string demangle(std::string name) {
int status = -4; // some arbitrary value to eliminate the compiler warning
......@@ -60,7 +59,6 @@ inline std::string demangle(std::string name) {
#else
inline std::string demangle(std::string name) { return name; }
#endif
}
struct EnforceNotMet : public std::exception {
std::exception_ptr exp_;
......
paddle/pybind/protobuf.cc
浏览文件 @ ce4e0e90
......@@ -202,9 +202,9 @@ void BindVarDsec(py::module &m) {
},
py::return_value_policy::reference)
.def("set_shape", &VarDescBind::SetShape)
.def("set_data_type", &VarDescBind::SetDataType)
.def("set_dtype", &VarDescBind::SetDataType)
.def("shape", &VarDescBind::Shape, py::return_value_policy::reference)
.def("data_type", &VarDescBind::GetDataType)
.def("dtype", &VarDescBind::GetDataType)
.def("lod_level", &VarDescBind::GetLodLevel)
.def("set_lod_level", &VarDescBind::SetLoDLevel)
.def("type", &VarDescBind::GetType)
......
paddle/pybind/pybind.cc
浏览文件 @ ce4e0e90
......@@ -293,6 +293,11 @@ All parameter, weight, gradient are variables in Paddle.
Prune(*prog_with_targets.Proto(), &pruned_desc);
return new ProgramDescBind(pruned_desc);
});
m.def("inference_optimize", [](ProgramDescBind &origin) {
ProgramDesc pruned_desc;
InferenceOptimize(*(origin.Proto()), &pruned_desc);
return new ProgramDescBind(pruned_desc);
});
m.def_submodule(
"var_names",
"The module will return special predefined variable name in Paddle")
......
python/paddle/v2/fluid/evaluator.py
浏览文件 @ ce4e0e90
......@@ -8,7 +8,7 @@ def _clone_var_in_block_(block, var):
return block.create_var(
name=var.name,
shape=var.shape,
dtype=var.data_type,
dtype=var.dtype,
type=var.type,
lod_level=var.lod_level,
persistable=True)
......@@ -33,6 +33,9 @@ class Evaluator(object):
else:
self._main_program = g_main_program
def states(self):
return self._states
def _update_ops(self, *args, **kwargs):
"""
append update ops to the global states
......@@ -57,7 +60,7 @@ class Evaluator(object):
attrs={
"shape": g_var.shape,
"value": .0,
"data_type": 5,
"dtype": 5,
})
block.append_op(
type="scale", inputs={"X": zeros}, outputs={"Out": g_var})
......@@ -93,7 +96,7 @@ class Accuracy(Evaluator):
def _update_ops(self, input, label, k=1, **kwargs):
block = self._main_program.global_block()
topk_out = block.create_var(dtype=input.data_type)
topk_out = block.create_var(dtype=input.dtype)
topk_indices = block.create_var(dtype="int64")
block.append_op(
type="top_k",
......@@ -122,16 +125,16 @@ class Accuracy(Evaluator):
inputs={"X": [self._states["Total"]]},
outputs={"Out": [self._states["Total"]]},
attrs={
"in_data_type": 5, # float32
"out_data_type": 2, #int32
"in_dtype": 5, # float32
"out_dtype": 2, # int32
})
block.append_op(
type="cast",
inputs={"X": [self._states["Correct"]]},
outputs={"Out": [self._states["Correct"]]},
attrs={
"in_data_type": 5,
"out_data_type": 2,
"in_dtype": 5,
"out_dtype": 2,
})
block.append_op(
......@@ -153,7 +156,7 @@ class Accuracy(Evaluator):
else:
eval_program = Program()
block = eval_program.global_block()
eval_out = block.create_var(dtype=self._states["Total"].data_type)
eval_out = block.create_var(dtype=self._states["Total"].dtype)
e_total = _clone_var_in_block_(block, self._states["Total"])
e_correct = _clone_var_in_block_(block, self._states["Correct"])
block.append_op(
......@@ -161,16 +164,16 @@ class Accuracy(Evaluator):
inputs={"X": [e_total]},
outputs={"Out": [e_total]},
attrs={
"in_data_type": 2, #int32
"out_data_type": 5, #float32
"in_dtype": 2, # int32
"out_dtype": 5, # float32
})
block.append_op(
type="cast",
inputs={"X": [e_correct]},
outputs={"Out": [e_correct]},
attrs={
"in_data_type": 2,
"out_data_type": 5,
"in_dtype": 2,
"out_dtype": 5,
})
block.append_op(
type="elementwise_div",
......
python/paddle/v2/fluid/framework.py
浏览文件 @ ce4e0e90
......@@ -99,9 +99,9 @@ class Variable(object):
if not isinstance(dtype, core.DataType):
dtype = convert_np_dtype_to_dtype_(dtype)
if is_new_var:
self.desc.set_data_type(dtype)
self.desc.set_dtype(dtype)
else:
old_dtype = self.data_type
old_dtype = self.dtype
if dtype != old_dtype:
raise ValueError("Variable {0} has been created before. "
"The previous data type is {1}; the new "
......@@ -162,8 +162,8 @@ class Variable(object):
return tuple(self.desc.shape())
@property
def data_type(self):
return self.desc.data_type()
def dtype(self):
return self.desc.dtype()
@property
def lod_level(self):
......@@ -511,6 +511,13 @@ class Program(object):
res.sync_with_cpp()
return res
def inference_optimize(self):
res = Program()
res.desc = core.inference_optimize(self.desc)
res.blocks = [Block(res, i) for i in xrange(res.desc.num_blocks())]
res.sync_with_cpp()
return res
@staticmethod
def parse_from_string(binary_str):
p = Program()
......
python/paddle/v2/fluid/initializer.py
浏览文件 @ ce4e0e90
......@@ -93,7 +93,7 @@ class ConstantInitializer(Initializer):
outputs={"Out": var},
attrs={
"shape": var.shape,
"data_type": int(var.data_type),
"dtype": int(var.dtype),
"value": self._value
})
var.op = op
......@@ -140,7 +140,7 @@ class UniformInitializer(Initializer):
outputs={"Out": var},
attrs={
"shape": var.shape,
"data_type": int(var.data_type),
"dtype": int(var.dtype),
"min": self._low,
"max": self._high,
"seed": self._seed
......@@ -188,7 +188,7 @@ class NormalInitializer(Initializer):
outputs={"Out": var},
attrs={
"shape": var.shape,
"data_type": int(var.data_type),
"dtype": int(var.dtype),
"mean": self._mean,
"std": self._std_dev,
"seed": self._seed
......@@ -265,7 +265,7 @@ class XavierInitializer(Initializer):
outputs={"Out": var},
attrs={
"shape": var.shape,
"data_type": int(var.data_type),
"dtype": int(var.dtype),
"min": -limit,
"max": limit,
"seed": self._seed
......@@ -278,7 +278,7 @@ class XavierInitializer(Initializer):
outputs={"Out": var},
attrs={
"shape": var.shape,
"data_type": int(var.data_type),
"dtype": int(var.dtype),
"mean": 0.0,
"std": std,
"seed": self._seed
......@@ -348,7 +348,7 @@ class MSRAInitializer(Initializer):
outputs={"Out": var},
attrs={
"shape": var.shape,
"data_type": int(var.data_type),
"dtype": int(var.dtype),
"min": -limit,
"max": limit,
"seed": self._seed
......@@ -361,7 +361,7 @@ class MSRAInitializer(Initializer):
outputs={"Out": var},
attrs={
"shape": var.shape,
"data_type": int(var.data_type),
"dtype": int(var.dtype),
"mean": 0.0,
"std": std,
"seed": self._seed
......
python/paddle/v2/fluid/io.py
浏览文件 @ ce4e0e90
......@@ -6,7 +6,8 @@ from paddle.v2.fluid.framework import Program, Parameter, g_main_program, \
__all__ = [
'save_vars', 'save_params', 'save_persistables', 'load_vars', 'load_params',
'load_persistables', "save_inference_model", "load_inference_model"
'load_persistables', "save_inference_model", "load_inference_model",
"get_inference_program"
]
......@@ -23,7 +24,7 @@ def _clone_var_in_block_(block, var):
return block.create_var(
name=var.name,
shape=var.shape,
dtype=var.data_type,
dtype=var.dtype,
type=var.type,
lod_level=var.lod_level,
persistable=True)
......@@ -151,6 +152,17 @@ def load_persistables(executor, dirname, main_program=None):
predicate=is_persistable)
def get_inference_program(target_vars, main_program=None):
if main_program is None:
main_program = g_main_program
if not isinstance(target_vars, list):
target_vars = [target_vars]
pruned_program = main_program.prune(targets=target_vars)
inference_program = pruned_program.inference_optimize()
return inference_program
def save_inference_model(dirname,
feeded_var_names,
target_vars,
......@@ -177,13 +189,14 @@ def save_inference_model(dirname,
if not os.path.isdir(dirname):
os.makedirs(dirname)
pruned_program = main_program.prune(target_vars)
pruned_program = main_program.prune(targets=target_vars)
inference_program = pruned_program.inference_optimize()
fetch_var_names = [v.name for v in target_vars]
model_file_name = dirname + "/__model__"
with open(model_file_name, "w") as f:
pickle.dump({
"program_desc_str": pruned_program.desc.serialize_to_string(),
"program_desc_str": inference_program.desc.serialize_to_string(),
"feed_var_names": feeded_var_names,
"fetch_var_names": fetch_var_names
}, f, -1)
......
python/paddle/v2/fluid/layer_helper.py
浏览文件 @ ce4e0e90
......@@ -108,8 +108,8 @@ class LayerHelper(object):
dtype = None
for each in inputs:
if dtype is None:
dtype = each.data_type
elif dtype != each.data_type:
dtype = each.dtype
elif dtype != each.dtype:
raise ValueError("Data Type mismatch")
return dtype
......@@ -149,7 +149,7 @@ class LayerHelper(object):
self.startup_program.global_block().create_var(
name=var.name,
type=var.type,
dtype=var.data_type,
dtype=var.dtype,
shape=var.shape,
persistable=True,
initializer=initializer)
......@@ -180,10 +180,10 @@ class LayerHelper(object):
b = self.create_parameter(
attr=bias_attr,
shape=size,
dtype=input_var.data_type,
dtype=input_var.dtype,
suffix='b',
initializer=bias_initializer)
tmp = self.create_tmp_variable(dtype=input_var.data_type)
tmp = self.create_tmp_variable(dtype=input_var.dtype)
self.append_op(
type='elementwise_add',
inputs={'X': [input_var],
......@@ -198,7 +198,7 @@ class LayerHelper(object):
return input_var
if isinstance(act, basestring):
act = {'type': act}
tmp = self.create_tmp_variable(dtype=input_var.data_type)
tmp = self.create_tmp_variable(dtype=input_var.dtype)
act_type = act.pop('type')
self.append_op(
type=act_type,
......
python/paddle/v2/fluid/layers.py
浏览文件 @ ce4e0e90
......@@ -114,7 +114,7 @@ def embedding(input,
is_sparse=False,
param_initializer=None,
param_attr=None,
data_type='float32',
dtype='float32',
main_program=None,
startup_program=None):
"""
......@@ -125,7 +125,7 @@ def embedding(input,
size: The size of the layer
is_sparse: A flag that decleares whether the input is sparse
param_attr: Parameters for this layer
data_type: The type of data : float32, float_16, int etc
dtype: The type of data : float32, float_16, int etc
main_program: Name of the main program that calls this
startup_program: Name of the startup program
......@@ -145,9 +145,9 @@ def embedding(input,
w = helper.create_parameter(
attr=helper.param_attr,
shape=size,
dtype=data_type,
dtype=dtype,
initializer=param_initializer or _get_default_param_initializer())
tmp = helper.create_tmp_variable(data_type)
tmp = helper.create_tmp_variable(dtype)
helper.append_op(
type='lookup_table',
inputs={'Ids': input,
......@@ -167,23 +167,23 @@ def dynamic_lstm(input,
gate_activation='sigmoid',
cell_activation='tanh',
candidate_activation='tanh',
data_type='float32',
dtype='float32',
main_program=None,
startup_program=None):
helper = LayerHelper('lstm', **locals())
size = size / 4
weight = helper.create_parameter(
attr=helper.param_attr, shape=[size, 4 * size], dtype=data_type)
attr=helper.param_attr, shape=[size, 4 * size], dtype=dtype)
bias_size = [1, 7 * size]
if not use_peepholes:
bias_size[1] = 4 * size
bias = helper.create_parameter(
attr=helper.bias_attr, shape=bias_size, dtype=data_type, suffix='b')
attr=helper.bias_attr, shape=bias_size, dtype=dtype, suffix='b')
hidden = helper.create_tmp_variable(data_type)
cell = helper.create_tmp_variable(data_type)
batch_gate = helper.create_tmp_variable(data_type)
batch_cell_pre_act = helper.create_tmp_variable(data_type)
hidden = helper.create_tmp_variable(dtype)
cell = helper.create_tmp_variable(dtype)
batch_gate = helper.create_tmp_variable(dtype)
batch_cell_pre_act = helper.create_tmp_variable(dtype)
helper.append_op(
type='lstm',
......@@ -209,7 +209,7 @@ def dynamic_lstm(input,
def data(name,
shape,
append_batch_size=True,
data_type='float32',
dtype='float32',
type=core.VarDesc.VarType.LOD_TENSOR,
main_program=None,
startup_program=None,
......@@ -221,7 +221,7 @@ def data(name,
name: The name/alias of the function
shape: Tuple declaring the shape.
append_batch_size: Whether or not to append the data as a batch.
data_type: The type of data : float32, float_16, int etc
dtype: The type of data : float32, float_16, int etc
type: The output type. By default it is LOD_TENSOR.
main_program: Name of the main program that calls this
startup_program: Name of the startup program
......@@ -251,7 +251,7 @@ def data(name,
return helper.create_global_variable(
name=name,
shape=shape,
dtype=data_type,
dtype=dtype,
type=type,
stop_gradient=stop_gradient)
......@@ -362,9 +362,9 @@ def _create_op_func_(op_type):
o_name = not_intermediate_outputs[0].name
intermediate_output_names = [output.name for output in intermediate_outputs]
def infer_and_check_data_type(op_proto, **kwargs):
def infer_and_check_dtype(op_proto, **kwargs):
"""
This function performs the sanity check for data_type and
This function performs the sanity check for dtype and
instance type.
"""
dtype = None
......@@ -379,8 +379,8 @@ def _create_op_func_(op_type):
op_type))
if dtype is None:
dtype = each.data_type
elif dtype != each.data_type:
dtype = each.dtype
elif dtype != each.dtype:
raise ValueError(
"operator {0} must input same dtype".format(op_type))
......@@ -389,7 +389,7 @@ def _create_op_func_(op_type):
def func(**kwargs):
helper = LayerHelper(op_type, **kwargs)
dtype = infer_and_check_data_type(op_proto, **kwargs)
dtype = infer_and_check_dtype(op_proto, **kwargs)
inputs = dict()
for ipt in op_proto.inputs:
......@@ -426,19 +426,19 @@ _create_op_func_('reshape')
_create_op_func_('transpose')
def cast(x, data_type, main_program=None):
def cast(x, dtype, main_program=None):
"""
This function takes in the input with input_data_type
and casts it to the output_data_type as the output.
This function takes in the input with input_dtype
and casts it to the output_dtype as the output.
"""
helper = LayerHelper('cast', **locals())
out = helper.create_tmp_variable(dtype=data_type)
out = helper.create_tmp_variable(dtype=dtype)
helper.append_op(
type='cast',
inputs={'X': [x]},
outputs={'Out': [out]},
attrs={'in_data_type': x.data_type,
'out_data_type': out.data_type})
attrs={'in_dtype': x.dtype,
'out_dtype': out.dtype})
return out
......@@ -519,8 +519,8 @@ def split_lod_tensor(input,
main_program=None,
startup_program=None):
helper = LayerHelper('split_lod_tensor', **locals())
out_true = helper.create_tmp_variable(dtype=input.data_type)
out_false = helper.create_tmp_variable(dtype=input.data_type)
out_true = helper.create_tmp_variable(dtype=input.dtype)
out_false = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op(
type='split_lod_tensor',
inputs={
......@@ -541,7 +541,7 @@ def merge_lod_tensor(in_true,
main_program=None,
startup_program=None):
helper = LayerHelper('merge_lod_tensor', **locals())
out = helper.create_tmp_variable(dtype=in_true.data_type)
out = helper.create_tmp_variable(dtype=in_true.dtype)
helper.append_op(
type='merge_lod_tensor',
inputs={'X': x,
......@@ -559,9 +559,9 @@ def cos_sim(X, Y, **kwargs):
X and Y and returns that as the output.
"""
helper = LayerHelper('cos_sim', **kwargs)
out = helper.create_tmp_variable(dtype=X.data_type)
xnorm = helper.create_tmp_variable(dtype=X.data_type)
ynorm = helper.create_tmp_variable(dtype=X.data_type)
out = helper.create_tmp_variable(dtype=X.dtype)
xnorm = helper.create_tmp_variable(dtype=X.dtype)
ynorm = helper.create_tmp_variable(dtype=X.dtype)
helper.append_op(
type='cos_sim',
inputs={'X': [X],
......@@ -577,7 +577,7 @@ def cross_entropy(input, label, **kwargs):
This function computes cross_entropy using the input and label.
"""
helper = LayerHelper('cross_entropy', **kwargs)
out = helper.create_tmp_variable(dtype=input.data_type)
out = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op(
type='cross_entropy',
inputs={'X': [input],
......@@ -593,14 +593,14 @@ def square_error_cost(input, label, **kwargs):
The output is appending the op to do the above.
"""
helper = LayerHelper('square_error_cost', **kwargs)
minus_out = helper.create_tmp_variable(dtype=input.data_type)
minus_out = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op(
type='elementwise_sub',
inputs={'X': [input],
'Y': [label]},
outputs={'Out': [minus_out]})
square_out = helper.create_tmp_variable(dtype=input.data_type)
square_out = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op(
type='square', inputs={'X': [minus_out]}, outputs={'Y': [square_out]})
return square_out
......@@ -612,7 +612,7 @@ def accuracy(input, label, k=1, **kwargs):
The output is the top_k inputs and their indices.
"""
helper = LayerHelper("accuracy", **kwargs)
topk_out = helper.create_tmp_variable(dtype=input.data_type)
topk_out = helper.create_tmp_variable(dtype=input.dtype)
topk_indices = helper.create_tmp_variable(dtype="int64")
helper.append_op(
type="top_k",
......@@ -883,12 +883,12 @@ def batch_norm(input,
initializer=ConstantInitializer(0.0))
mean = helper.create_global_variable(
dtype=input.data_type, shape=param_shape, persistable=True)
dtype=input.dtype, shape=param_shape, persistable=True)
helper.set_variable_initializer(
var=mean, initializer=ConstantInitializer(0.0))
variance = helper.create_global_variable(
dtype=input.data_type, shape=param_shape, persistable=True)
dtype=input.dtype, shape=param_shape, persistable=True)
helper.set_variable_initializer(
var=variance, initializer=ConstantInitializer(1.0))
......@@ -927,8 +927,8 @@ def batch_norm(input,
def beam_search_decode(ids, scores, main_program=None, startup_program=None):
helper = LayerHelper('beam_search_decode', **locals())
sentence_ids = helper.create_tmp_variable(dtype=ids.data_type)
sentence_scores = helper.create_tmp_variable(dtype=ids.data_type)
sentence_ids = helper.create_tmp_variable(dtype=ids.dtype)
sentence_scores = helper.create_tmp_variable(dtype=ids.dtype)
helper.append_op(
type="beam_search_decode",
......@@ -1066,7 +1066,7 @@ class StaticRNN(object):
boot_var = parent_block.create_var(
name=var_name,
shape=shape,
dtype=batch_ref.data_type,
dtype=batch_ref.dtype,
persistable=False)
parent_block.append_op(
......@@ -1076,7 +1076,7 @@ class StaticRNN(object):
attrs={
'value': init_value,
'shape': boot_var.shape,
'data_type': boot_var.data_type,
'dtype': boot_var.dtype,
'input_dim_idx': ref_batch_dim_idx,
'output_dim_idx': init_batch_dim_idx
})
......@@ -1085,7 +1085,7 @@ class StaticRNN(object):
else:
pre_mem = self.helper.create_variable(
name=unique_name("@".join([self.helper.name, "mem"])),
dtype=init.data_type,
dtype=init.dtype,
shape=init.shape)
self.memories[pre_mem.name] = StaticRNNMemoryLink(
init=init, pre_mem=pre_mem)
......@@ -1101,10 +1101,7 @@ class StaticRNN(object):
raise ValueError("Static RNN only take fix seq_len input")
ipt = self.helper.create_variable(
name=x.name,
dtype=x.data_type,
shape=list(x.shape[1:]),
type=x.type)
name=x.name, dtype=x.dtype, shape=list(x.shape[1:]), type=x.type)
self.inputs.append(ipt)
return ipt
......@@ -1113,17 +1110,17 @@ class StaticRNN(object):
if not isinstance(o, Variable):
raise TypeError("step output takes a Variable")
tmp_o = self.helper.create_tmp_variable(dtype=o.data_type)
tmp_o = self.helper.create_tmp_variable(dtype=o.dtype)
self.helper.append_op(
type='rnn_memory_helper',
inputs={'X': [o]},
outputs={'Out': tmp_o},
attrs={'data_type': o.data_type})
attrs={'dtype': o.dtype})
out_var = self.parent_block().create_var(
name=tmp_o.name,
shape=[self.seq_len] + list(tmp_o.shape),
dtype=tmp_o.data_type)
dtype=tmp_o.dtype)
self.outputs.append(out_var)
......@@ -1195,13 +1192,13 @@ class StaticRNN(object):
pre_memories.append(mem.pre_mem.name)
mem_var = rnn_block.var(mem.mem.name)
assert isinstance(mem_var, Variable)
new_mem = self.helper.create_tmp_variable(dtype=mem_var.data_type)
new_mem = self.helper.create_tmp_variable(dtype=mem_var.dtype)
rnn_block.append_op(
type='rnn_memory_helper',
inputs={'X': [mem_var]},
outputs={'Out': [new_mem]},
attrs={'data_type': mem_var.data_type})
attrs={'dtype': mem_var.dtype})
memories.append(new_mem.name)
......@@ -1251,7 +1248,7 @@ class While(object):
if not isinstance(cond, Variable):
raise TypeError("condition should be a variable")
assert isinstance(cond, Variable)
if cond.data_type != core.DataType.BOOL:
if cond.dtype != core.DataType.BOOL:
raise TypeError("condition should be a bool variable")
if reduce(lambda a, b: a * b, cond.shape, 1) != 1:
raise TypeError("condition should be a bool scalar")
......@@ -1323,9 +1320,9 @@ def lstm(x,
main_program=main_program,
startup_program=startup_program)
data_type = x.data_type
c = helper.create_tmp_variable(data_type)
h = helper.create_tmp_variable(data_type)
dtype = x.dtype
c = helper.create_tmp_variable(dtype)
h = helper.create_tmp_variable(dtype)
helper.append_op(
type='lstm_unit',
......@@ -1367,7 +1364,7 @@ def lod_tensor_to_array(x, table, main_program=None):
array = helper.create_variable(
name=unique_name("lod_tensor_to_array"),
type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
dtype=x.data_type)
dtype=x.dtype)
helper.append_op(
type='lod_tensor_to_array',
inputs={'X': x,
......@@ -1382,7 +1379,7 @@ def array_to_lod_tensor(x, table, main_program=None):
LOD_Tensor.
"""
helper = LayerHelper("array_to_lod_tensor", **locals())
tmp = helper.create_tmp_variable(dtype=x.data_type)
tmp = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type="array_to_lod_tensor",
inputs={'X': x,
......@@ -1394,7 +1391,7 @@ def array_to_lod_tensor(x, table, main_program=None):
def fill_constant(shape, dtype, value, main_program=None, startup_program=None):
"""
This function creates a tensor , with shape as mentioned in the input and
specified data_type and fills this up with a constant value that
specified dtype and fills this up with a constant value that
comes in the input. It also sets the stop_gradient to be True.
"""
helper = LayerHelper("fill_constant", **locals())
......@@ -1403,11 +1400,9 @@ def fill_constant(shape, dtype, value, main_program=None, startup_program=None):
type='fill_constant',
inputs={},
outputs={'Out': [out]},
attrs={
'shape': shape,
'data_type': out.data_type,
'value': float(value)
})
attrs={'shape': shape,
'dtype': out.dtype,
'value': float(value)})
out.stop_gradient = True
return out
......@@ -1428,7 +1423,7 @@ def fill_constant_batch_size_like(input,
outputs={'Out': [out]},
attrs={
'shape': shape,
'data_type': out.data_type,
'dtype': out.dtype,
'value': float(value),
'input_dim_idx': input_dim_idx,
'output_dim_idx': output_dim_idx
......@@ -1461,7 +1456,7 @@ def increment(x, value=1.0, in_place=True, main_program=None):
"""
helper = LayerHelper("increment", **locals())
if not in_place:
out = helper.create_tmp_variable(dtype=x.data_type)
out = helper.create_tmp_variable(dtype=x.dtype)
else:
out = x
helper.append_op(
......@@ -1482,7 +1477,7 @@ def array_write(x, i, array=None, main_program=None):
array = helper.create_variable(
name="{0}.out".format(helper.name),
type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
dtype=x.data_type)
dtype=x.dtype)
helper.append_op(
type='write_to_array',
inputs={'X': [x],
......@@ -1521,7 +1516,7 @@ def array_read(array, i, main_program=None):
array,
Variable) or array.type != core.VarDesc.VarType.LOD_TENSOR_ARRAY:
raise TypeError("array should be tensor array vairable")
out = helper.create_tmp_variable(dtype=array.data_type)
out = helper.create_tmp_variable(dtype=array.dtype)
helper.append_op(
type='read_from_array',
inputs={'X': [array],
......@@ -1536,7 +1531,7 @@ def shrink_memory(x, i, table, main_program=None):
as mentioned in the input parameter.
"""
helper = LayerHelper('shrink_memory', **locals())
out = helper.create_tmp_variable(dtype=x.data_type)
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type='shrink_rnn_memory',
inputs={'X': [x],
......@@ -1698,11 +1693,11 @@ class IfElse(object):
parent_block = self.parent_block()
out_true = parent_block.create_var(
name=unique_name('ifelse_input' + self.helper.name),
dtype=x.data_type)
dtype=x.dtype)
out_false = parent_block.create_var(
name=unique_name('ifelse_input' + self.helper.name),
dtype=x.data_type)
dtype=x.dtype)
parent_block.append_op(
type='split_lod_tensor',
inputs={
......@@ -1744,7 +1739,7 @@ class IfElse(object):
# create outside tensor
outside_out = parent_block.create_var(
name=unique_name("_".join([self.helper.name, 'output'])),
dtype=each_out.data_type)
dtype=each_out.dtype)
out_table.append(outside_out)
# assign local var to outside
......
python/paddle/v2/fluid/optimizer.py
浏览文件 @ ce4e0e90
......@@ -92,7 +92,7 @@ class Optimizer(object):
var = self.helper.create_global_variable(
name=unique_name(name),
persistable=True,
dtype=dtype or param.data_type,
dtype=dtype or param.dtype,
type=param.type,
shape=param.shape)
self.helper.set_variable_initializer(
......@@ -202,7 +202,7 @@ class Optimizer(object):
"""
params_grads = append_backward_ops(loss, parameter_list, no_grad_set or
set())
# Add regularization if any
# Add regularization if any
params_grads = append_regularization_ops(params_grads)
optimize_ops = self.create_optimization_pass(params_grads, loss,
startup_program)
......
python/paddle/v2/fluid/tests/book/test_fit_a_line.py
浏览文件 @ ce4e0e90
......@@ -7,11 +7,11 @@ from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.io import save_persistables, load_persistables
from paddle.v2.fluid.optimizer import SGDOptimizer
x = layers.data(name='x', shape=[13], data_type='float32')
x = layers.data(name='x', shape=[13], dtype='float32')
y_predict = layers.fc(input=x, size=1, act=None)
y = layers.data(name='y', shape=[1], data_type='float32')
y = layers.data(name='y', shape=[1], dtype='float32')
cost = layers.square_error_cost(input=y_predict, label=y)
avg_cost = layers.mean(x=cost)
......
python/paddle/v2/fluid/tests/book/test_image_classification_train.py
浏览文件 @ ce4e0e90
......@@ -5,6 +5,7 @@ import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.nets as nets
import paddle.v2.fluid.evaluator as evaluator
from paddle.v2.fluid.io import get_inference_program
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.initializer import XavierInitializer
from paddle.v2.fluid.optimizer import AdamOptimizer
......@@ -90,8 +91,8 @@ def vgg16_bn_drop(input):
classdim = 10
data_shape = [3, 32, 32]
images = layers.data(name='pixel', shape=data_shape, data_type='float32')
label = layers.data(name='label', shape=[1], data_type='int64')
images = layers.data(name='pixel', shape=data_shape, dtype='float32')
label = layers.data(name='label', shape=[1], dtype='int64')
# Add neural network config
# option 1. resnet
......@@ -116,9 +117,11 @@ PASS_NUM = 1
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.cifar.train10(), buf_size=128 * 10),
paddle.dataset.cifar.train10(), buf_size=BATCH_SIZE * 10),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE)
place = core.CPUPlace()
exe = Executor(place)
......@@ -149,10 +152,41 @@ for pass_id in range(PASS_NUM):
loss = np.array(outs[0])
acc = np.array(outs[1])
pass_acc = accuracy.eval(exe)
batch_id = batch_id + 1
test_accuracy, test_acc_out = evaluator.accuracy(
input=predict, label=label)
test_target = [avg_cost, test_acc_out] + test_accuracy.states().values()
inference_program = get_inference_program(test_target)
test_accuracy.reset(exe)
for data in test_reader():
x_data = np.array(map(lambda x: x[0].reshape(data_shape),
data)).astype("float32")
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
y_data = np.expand_dims(y_data, axis=1)
tensor_x = core.LoDTensor()
tensor_x.set(x_data, place)
tensor_y = core.LoDTensor()
tensor_y.set(y_data, place)
outs = exe.run(inference_program,
feed={'pixel': tensor_x,
'label': tensor_y},
fetch_list=[avg_cost, test_acc_out])
out = np.array(outs[0])
acc = np.array(outs[1])
test_pass_acc = test_accuracy.eval(exe)
print("pass_id:" + str(pass_id) + " batch_id:" + str(batch_id) +
" loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
pass_acc))
batch_id = batch_id + 1
pass_acc) + " test_pass_acc:" + str(test_pass_acc))
if batch_id > 1:
# this model is slow, so if we can train two mini batch, we think it works properly.
......
python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py
浏览文件 @ ce4e0e90
......@@ -34,26 +34,26 @@ def load_parameter(file_name, h, w):
def db_lstm():
# 8 features
word = layers.data(name='word_data', shape=[1], data_type='int64')
predicate = layers.data(name='verb_data', shape=[1], data_type='int64')
ctx_n2 = layers.data(name='ctx_n2_data', shape=[1], data_type='int64')
ctx_n1 = layers.data(name='ctx_n1_data', shape=[1], data_type='int64')
ctx_0 = layers.data(name='ctx_0_data', shape=[1], data_type='int64')
ctx_p1 = layers.data(name='ctx_p1_data', shape=[1], data_type='int64')
ctx_p2 = layers.data(name='ctx_p2_data', shape=[1], data_type='int64')
mark = layers.data(name='mark_data', shape=[1], data_type='int64')
word = layers.data(name='word_data', shape=[1], dtype='int64')
predicate = layers.data(name='verb_data', shape=[1], dtype='int64')
ctx_n2 = layers.data(name='ctx_n2_data', shape=[1], dtype='int64')
ctx_n1 = layers.data(name='ctx_n1_data', shape=[1], dtype='int64')
ctx_0 = layers.data(name='ctx_0_data', shape=[1], dtype='int64')
ctx_p1 = layers.data(name='ctx_p1_data', shape=[1], dtype='int64')
ctx_p2 = layers.data(name='ctx_p2_data', shape=[1], dtype='int64')
mark = layers.data(name='mark_data', shape=[1], dtype='int64')
predicate_embedding = layers.embedding(
input=predicate,
size=[pred_len, word_dim],
data_type='float32',
dtype='float32',
is_sparse=IS_SPARSE,
param_attr={'name': 'vemb'})
mark_embedding = layers.embedding(
input=mark,
size=[mark_dict_len, mark_dim],
data_type='float32',
dtype='float32',
is_sparse=IS_SPARSE)
word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
......@@ -125,7 +125,7 @@ def to_lodtensor(data, place):
def main():
# define network topology
feature_out = db_lstm()
target = layers.data(name='target', shape=[1], data_type='int64')
target = layers.data(name='target', shape=[1], dtype='int64')
crf_cost = layers.linear_chain_crf(
input=feature_out,
label=target,
......
python/paddle/v2/fluid/tests/book/test_recognize_digits_conv.py
浏览文件 @ ce4e0e90
......@@ -8,8 +8,8 @@ import paddle.v2.fluid.nets as nets
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.optimizer import AdamOptimizer
images = layers.data(name='pixel', shape=[1, 28, 28], data_type='float32')
label = layers.data(name='label', shape=[1], data_type='int64')
images = layers.data(name='pixel', shape=[1, 28, 28], dtype='float32')
label = layers.data(name='label', shape=[1], dtype='int64')
conv_pool_1 = nets.simple_img_conv_pool(
input=images,
filter_size=5,
......
python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py
浏览文件 @ ce4e0e90
......@@ -4,13 +4,14 @@ import paddle.v2.fluid.core as core
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.evaluator as evaluator
from paddle.v2.fluid.io import get_inference_program
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.initializer import UniformInitializer
from paddle.v2.fluid.optimizer import MomentumOptimizer
from paddle.v2.fluid.regularizer import L2DecayRegularizer
BATCH_SIZE = 128
image = layers.data(name='x', shape=[784], data_type='float32')
image = layers.data(name='x', shape=[784], dtype='float32')
param_attr = {
'name': None,
......@@ -27,7 +28,7 @@ predict = layers.fc(input=hidden2,
act='softmax',
param_attr=param_attr)
label = layers.data(name='y', shape=[1], data_type='int64')
label = layers.data(name='y', shape=[1], dtype='int64')
cost = layers.cross_entropy(input=predict, label=label)
avg_cost = layers.mean(x=cost)
......@@ -42,6 +43,8 @@ train_reader = paddle.batch(
paddle.dataset.mnist.train(), buf_size=8192),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=128)
place = core.CPUPlace()
exe = Executor(place)
......@@ -69,8 +72,36 @@ for pass_id in range(PASS_NUM):
acc = np.array(outs[1])
pass_acc = accuracy.eval(exe)
if pass_acc > 0.7:
test_accuracy, test_acc_out = evaluator.accuracy(
input=predict, label=label)
test_target = [avg_cost, test_acc_out] + test_accuracy.states().values()
inference_program = get_inference_program(test_target)
test_accuracy.reset(exe)
for data in test_reader():
x_data = np.array(map(lambda x: x[0], data)).astype("float32")
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
y_data = np.expand_dims(y_data, axis=1)
tensor_x = core.LoDTensor()
tensor_x.set(x_data, place)
tensor_y = core.LoDTensor()
tensor_y.set(y_data, place)
outs = exe.run(inference_program,
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_cost, test_acc_out])
out = np.array(outs[0])
acc = np.array(outs[1])
test_pass_acc = test_accuracy.eval(exe)
print("pass_id=" + str(pass_id) + " train_cost=" + str(
out) + " train_acc=" + str(acc) + " train_pass_acc=" + str(pass_acc)
+ " test_acc=" + str(test_pass_acc))
if test_pass_acc > 0.7:
exit(0)
# print("pass_id=" + str(pass_id) + " auc=" +
# str(acc) + " pass_acc=" + str(pass_acc))
exit(1)
python/paddle/v2/fluid/tests/book/test_recommender_system.py
浏览文件 @ ce4e0e90
......@@ -18,11 +18,11 @@ def get_usr_combined_features():
USR_DICT_SIZE = paddle.dataset.movielens.max_user_id() + 1
uid = layers.data(name='user_id', shape=[1], data_type='int64')
uid = layers.data(name='user_id', shape=[1], dtype='int64')
usr_emb = layers.embedding(
input=uid,
data_type='float32',
dtype='float32',
size=[USR_DICT_SIZE, 32],
param_attr={'name': 'user_table'},
is_sparse=IS_SPARSE)
......@@ -31,7 +31,7 @@ def get_usr_combined_features():
USR_GENDER_DICT_SIZE = 2
usr_gender_id = layers.data(name='gender_id', shape=[1], data_type='int64')
usr_gender_id = layers.data(name='gender_id', shape=[1], dtype='int64')
usr_gender_emb = layers.embedding(
input=usr_gender_id,
......@@ -42,7 +42,7 @@ def get_usr_combined_features():
usr_gender_fc = layers.fc(input=usr_gender_emb, size=16)
USR_AGE_DICT_SIZE = len(paddle.dataset.movielens.age_table)
usr_age_id = layers.data(name='age_id', shape=[1], data_type="int64")
usr_age_id = layers.data(name='age_id', shape=[1], dtype="int64")
usr_age_emb = layers.embedding(
input=usr_age_id,
......@@ -53,7 +53,7 @@ def get_usr_combined_features():
usr_age_fc = layers.fc(input=usr_age_emb, size=16)
USR_JOB_DICT_SIZE = paddle.dataset.movielens.max_job_id() + 1
usr_job_id = layers.data(name='job_id', shape=[1], data_type="int64")
usr_job_id = layers.data(name='job_id', shape=[1], dtype="int64")
usr_job_emb = layers.embedding(
input=usr_job_id,
......@@ -75,11 +75,11 @@ def get_mov_combined_features():
MOV_DICT_SIZE = paddle.dataset.movielens.max_movie_id() + 1
mov_id = layers.data(name='movie_id', shape=[1], data_type='int64')
mov_id = layers.data(name='movie_id', shape=[1], dtype='int64')
mov_emb = layers.embedding(
input=mov_id,
data_type='float32',
dtype='float32',
size=[MOV_DICT_SIZE, 32],
param_attr={'name': 'movie_table'},
is_sparse=IS_SPARSE)
......@@ -88,7 +88,7 @@ def get_mov_combined_features():
CATEGORY_DICT_SIZE = len(paddle.dataset.movielens.movie_categories())
category_id = layers.data(name='category_id', shape=[1], data_type='int64')
category_id = layers.data(name='category_id', shape=[1], dtype='int64')
mov_categories_emb = layers.embedding(
input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=IS_SPARSE)
......@@ -98,7 +98,7 @@ def get_mov_combined_features():
MOV_TITLE_DICT_SIZE = len(paddle.dataset.movielens.get_movie_title_dict())
mov_title_id = layers.data(name='movie_title', shape=[1], data_type='int64')
mov_title_id = layers.data(name='movie_title', shape=[1], dtype='int64')
mov_title_emb = layers.embedding(
input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=IS_SPARSE)
......@@ -126,7 +126,7 @@ def model():
# need cos sim
inference = layers.cos_sim(X=usr_combined_features, Y=mov_combined_features)
label = layers.data(name='score', shape=[1], data_type='float32')
label = layers.data(name='score', shape=[1], dtype='float32')
square_cost = layers.square_error_cost(input=inference, label=label)
......
python/paddle/v2/fluid/tests/book/test_understand_sentiment_conv.py
浏览文件 @ ce4e0e90
......@@ -10,8 +10,8 @@ from paddle.v2.fluid.optimizer import AdamOptimizer
def convolution_net(input_dim, class_dim=2, emb_dim=32, hid_dim=32):
data = layers.data(name="words", shape=[1], data_type="int64")
label = layers.data(name="label", shape=[1], data_type="int64")
data = layers.data(name="words", shape=[1], dtype="int64")
label = layers.data(name="label", shape=[1], dtype="int64")
emb = layers.embedding(input=data, size=[input_dim, emb_dim])
conv_3 = nets.sequence_conv_pool(
......
python/paddle/v2/fluid/tests/book/test_understand_sentiment_dynamic_lstm.py
浏览文件 @ ce4e0e90
......@@ -14,8 +14,8 @@ def stacked_lstm_net(input_dim,
hid_dim=512,
stacked_num=3):
assert stacked_num % 2 == 1
data = layers.data(name="words", shape=[1], data_type="int64")
label = layers.data(name="label", shape=[1], data_type="int64")
data = layers.data(name="words", shape=[1], dtype="int64")
label = layers.data(name="label", shape=[1], dtype="int64")
emb = layers.embedding(input=data, size=[input_dim, emb_dim])
# add bias attr
......
python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py
浏览文件 @ ce4e0e90
......@@ -12,19 +12,19 @@ def lstm_net(dict_dim, class_dim=2, emb_dim=32, seq_len=80, batch_size=50):
name="words",
shape=[seq_len * batch_size, 1],
append_batch_size=False,
data_type="int64")
dtype="int64")
label = layers.data(
name="label",
shape=[batch_size, 1],
append_batch_size=False,
data_type="int64")
dtype="int64")
emb = layers.embedding(input=data, size=[dict_dim, emb_dim])
emb = layers.reshape(x=emb, shape=[batch_size, seq_len, emb_dim])
emb = layers.transpose(x=emb, axis=[1, 0, 2])
c_pre_init = layers.fill_constant(
dtype=emb.data_type, shape=[batch_size, emb_dim], value=0.0)
dtype=emb.dtype, shape=[batch_size, emb_dim], value=0.0)
layer_1_out = layers.lstm(emb, c_pre_init=c_pre_init, hidden_dim=emb_dim)
layer_1_out = layers.transpose(x=layer_1_out, axis=[1, 0, 2])
......
python/paddle/v2/fluid/tests/book/test_word2vec.py
浏览文件 @ ce4e0e90
......@@ -16,34 +16,34 @@ IS_SPARSE = True
word_dict = paddle.dataset.imikolov.build_dict()
dict_size = len(word_dict)
first_word = layers.data(name='firstw', shape=[1], data_type='int64')
second_word = layers.data(name='secondw', shape=[1], data_type='int64')
third_word = layers.data(name='thirdw', shape=[1], data_type='int64')
forth_word = layers.data(name='forthw', shape=[1], data_type='int64')
next_word = layers.data(name='nextw', shape=[1], data_type='int64')
first_word = layers.data(name='firstw', shape=[1], dtype='int64')
second_word = layers.data(name='secondw', shape=[1], dtype='int64')
third_word = layers.data(name='thirdw', shape=[1], dtype='int64')
forth_word = layers.data(name='forthw', shape=[1], dtype='int64')
next_word = layers.data(name='nextw', shape=[1], dtype='int64')
embed_first = layers.embedding(
input=first_word,
size=[dict_size, EMBED_SIZE],
data_type='float32',
dtype='float32',
is_sparse=IS_SPARSE,
param_attr={'name': 'shared_w'})
embed_second = layers.embedding(
input=second_word,
size=[dict_size, EMBED_SIZE],
data_type='float32',
dtype='float32',
is_sparse=IS_SPARSE,
param_attr={'name': 'shared_w'})
embed_third = layers.embedding(
input=third_word,
size=[dict_size, EMBED_SIZE],
data_type='float32',
dtype='float32',
is_sparse=IS_SPARSE,
param_attr={'name': 'shared_w'})
embed_forth = layers.embedding(
input=forth_word,
size=[dict_size, EMBED_SIZE],
data_type='float32',
dtype='float32',
is_sparse=IS_SPARSE,
param_attr={'name': 'shared_w'})
......
python/paddle/v2/fluid/tests/op_test.py
浏览文件 @ ce4e0e90
......@@ -458,7 +458,7 @@ class OpTest(unittest.TestCase):
mean_inputs = map(block.var, output_names)
if len(mean_inputs) == 1:
loss = block.create_var(dtype=mean_inputs[0].data_type, shape=[1])
loss = block.create_var(dtype=mean_inputs[0].dtype, shape=[1])
op = block.append_op(
inputs={"X": mean_inputs}, outputs={"Out": loss}, type='mean')
op.desc.infer_var_type(block.desc)
......@@ -466,8 +466,7 @@ class OpTest(unittest.TestCase):
else:
avg_sum = []
for cur_loss in mean_inputs:
cur_avg_loss = block.create_var(
dtype=cur_loss.data_type, shape=[1])
cur_avg_loss = block.create_var(dtype=cur_loss.dtype, shape=[1])
op = block.append_op(
inputs={"X": [cur_loss]},
outputs={"Out": [cur_avg_loss]},
......@@ -476,13 +475,13 @@ class OpTest(unittest.TestCase):
op.desc.infer_shape(block.desc)
avg_sum.append(cur_avg_loss)
loss_sum = block.create_var(dtype=avg_sum[0].data_type, shape=[1])
loss_sum = block.create_var(dtype=avg_sum[0].dtype, shape=[1])
op_sum = block.append_op(
inputs={"X": avg_sum}, outputs={"Out": loss_sum}, type='sum')
op_sum.desc.infer_var_type(block.desc)
op_sum.desc.infer_shape(block.desc)
loss = block.create_var(dtype=loss_sum.data_type, shape=[1])
loss = block.create_var(dtype=loss_sum.dtype, shape=[1])
op_loss = block.append_op(
inputs={"X": loss_sum},
outputs={"Out": loss},
......
python/paddle/v2/fluid/tests/test_beam_search_decode_op.py
浏览文件 @ ce4e0e90
......@@ -35,15 +35,15 @@ class TestBeamSearchDecodeOp(unittest.TestCase):
self.append_lod_tensor(
scores, [[0, 3, 6], [0, 1, 2, 3, 4, 5, 6]],
np.array(
[1, 2, 3, 4, 5, 6], dtype="float32"))
[1, 2, 3, 4, 5, 6], dtype="float64"))
self.append_lod_tensor(
scores, [[0, 3, 6], [0, 1, 1, 3, 5, 5, 6]],
np.array(
[0, 1, 2, 3, 4, 5], dtype="float32"))
[0, 1, 2, 3, 4, 5], dtype="float64"))
self.append_lod_tensor(
scores, [[0, 3, 6], [0, 0, 1, 2, 3, 4, 5]],
np.array(
[0, 1, 2, 3, 4], dtype="float32"))
[0, 1, 2, 3, 4], dtype="float64"))
sentence_ids = self.scope.var("sentence_ids").get_tensor()
sentence_scores = self.scope.var("sentence_scores").get_tensor()
......
python/paddle/v2/fluid/tests/test_cast_op.py
浏览文件 @ ce4e0e90
......@@ -10,8 +10,8 @@ class TestCastOp(op_test.OpTest):
self.inputs = {'X': ipt.astype('float32')}
self.outputs = {'Out': ipt.astype('float64')}
self.attrs = {
'in_data_type': int(core.DataType.FP32),
'out_data_type': int(core.DataType.FP64)
'in_dtype': int(core.DataType.FP32),
'out_dtype': int(core.DataType.FP64)
}
self.op_type = 'cast'
......
python/paddle/v2/fluid/tests/test_conditional_block.py
浏览文件 @ ce4e0e90
......@@ -9,7 +9,7 @@ import numpy
class ConditionalBlock(unittest.TestCase):
def test_forward(self):
data = layers.data(name='X', shape=[1], data_type='float32')
data = layers.data(name='X', shape=[1], dtype='float32')
data.stop_gradient = False
cond = layers.ConditionalBlock(inputs=[data])
out = layers.create_tensor(dtype='float32')
......
python/paddle/v2/fluid/tests/test_dropout_op.py
浏览文件 @ ce4e0e90
......@@ -7,7 +7,7 @@ class TestDropoutOp(OpTest):
def setUp(self):
self.op_type = "dropout"
self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
self.attrs = {'dropout_prob': 0.0, 'is_training': True}
self.attrs = {'dropout_prob': 0.0, 'is_test': False}
self.outputs = {
'Out': self.inputs['X'],
'Mask': np.ones((32, 64)).astype('float32')
......@@ -24,7 +24,7 @@ class TestDropoutOp2(TestDropoutOp):
def setUp(self):
self.op_type = "dropout"
self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
self.attrs = {'dropout_prob': 1.0, 'is_training': True}
self.attrs = {'dropout_prob': 1.0, 'is_test': False}
self.outputs = {
'Out': np.zeros((32, 64)).astype('float32'),
'Mask': np.zeros((32, 64)).astype('float32')
......@@ -35,7 +35,7 @@ class TestDropoutOp3(TestDropoutOp):
def setUp(self):
self.op_type = "dropout"
self.inputs = {'X': np.random.random((32, 64, 2)).astype("float32")}
self.attrs = {'dropout_prob': 0.0, 'is_training': True}
self.attrs = {'dropout_prob': 0.0, 'is_test': False}
self.outputs = {
'Out': self.inputs['X'],
'Mask': np.ones((32, 64, 2)).astype('float32')
......@@ -46,7 +46,7 @@ class TestDropoutOp4(OpTest):
def setUp(self):
self.op_type = "dropout"
self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
self.attrs = {'dropout_prob': 0.35, 'is_training': False}
self.attrs = {'dropout_prob': 0.35, 'is_test': True}
self.outputs = {'Out': self.inputs['X'] * self.attrs['dropout_prob']}
def test_check_output(self):
......@@ -57,7 +57,7 @@ class TestDropoutOp5(OpTest):
def setUp(self):
self.op_type = "dropout"
self.inputs = {'X': np.random.random((32, 64, 3)).astype("float32")}
self.attrs = {'dropout_prob': 0.75, 'is_training': False}
self.attrs = {'dropout_prob': 0.75, 'is_test': True}
self.outputs = {'Out': self.inputs['X'] * self.attrs['dropout_prob']}
def test_check_output(self):
......
python/paddle/v2/fluid/tests/test_executor_and_mul.py
浏览文件 @ ce4e0e90
......@@ -8,11 +8,11 @@ import numpy
class TestExecutor(unittest.TestCase):
def test_mul(self):
a = data(name='a', shape=[784], data_type='float32')
a = data(name='a', shape=[784], dtype='float32')
b = data(
name='b',
shape=[784, 100],
data_type='float32',
dtype='float32',
append_batch_size=False)
out = mul(x=a, y=b)
place = core.CPUPlace()
......
python/paddle/v2/fluid/tests/test_image_classification_layer.py
浏览文件 @ ce4e0e90
......@@ -32,7 +32,7 @@ class TestLayer(unittest.TestCase):
images = layers.data(
name='pixel',
shape=[3, 48, 48],
data_type='float32',
dtype='float32',
main_program=main_program)
layers.batch_norm(
input=images,
......@@ -47,7 +47,7 @@ class TestLayer(unittest.TestCase):
images = layers.data(
name='pixel',
shape=[3, 48, 48],
data_type='float32',
dtype='float32',
main_program=main_program)
layers.dropout(
x=images,
......@@ -64,7 +64,7 @@ class TestLayer(unittest.TestCase):
images = layers.data(
name='pixel',
shape=[3, 48, 48],
data_type='float32',
dtype='float32',
main_program=main_program,
startup_program=startup_program)
conv1 = conv_block(images, 64, 2, [0.3, 0], main_program,
......@@ -80,13 +80,13 @@ class TestLayer(unittest.TestCase):
image1 = layers.data(
name='pixel1',
shape=[3, 48, 48],
data_type='float32',
dtype='float32',
main_program=main_program,
startup_program=startup_program)
image2 = layers.data(
name='pixel2',
shape=[3, 48, 48],
data_type='float32',
dtype='float32',
main_program=main_program,
startup_program=startup_program)
out = layers.elementwise_add(
......
python/paddle/v2/fluid/tests/test_inference_model_io.py
浏览文件 @ ce4e0e90
......@@ -19,13 +19,13 @@ class TestBook(unittest.TestCase):
x = layers.data(
name='x',
shape=[2],
data_type='float32',
dtype='float32',
main_program=program,
startup_program=init_program)
y = layers.data(
name='y',
shape=[1],
data_type='float32',
dtype='float32',
main_program=program,
startup_program=init_program)
......
python/paddle/v2/fluid/tests/test_layers.py
浏览文件 @ ce4e0e90
......@@ -9,11 +9,11 @@ class TestBook(unittest.TestCase):
def test_fit_a_line(self):
program = Program()
x = layers.data(
name='x', shape=[13], data_type='float32', main_program=program)
name='x', shape=[13], dtype='float32', main_program=program)
y_predict = layers.fc(input=x, size=1, act=None, main_program=program)
y = layers.data(
name='y', shape=[1], data_type='float32', main_program=program)
name='y', shape=[1], dtype='float32', main_program=program)
cost = layers.square_error_cost(
input=y_predict, label=y, main_program=program)
......@@ -28,12 +28,9 @@ class TestBook(unittest.TestCase):
# Change g_program, so the rest layers use `g_program`
images = layers.data(
name='pixel',
shape=[784],
data_type='float32',
main_program=program)
name='pixel', shape=[784], dtype='float32', main_program=program)
label = layers.data(
name='label', shape=[1], data_type='int32', main_program=program)
name='label', shape=[1], dtype='int32', main_program=program)
hidden1 = layers.fc(input=images,
size=128,
act='relu',
......@@ -58,7 +55,7 @@ class TestBook(unittest.TestCase):
images = layers.data(
name='pixel',
shape=[3, 48, 48],
data_type='int32',
dtype='int32',
main_program=program)
layers.conv2d(
input=images,
......@@ -74,10 +71,10 @@ class TestBook(unittest.TestCase):
images = layers.data(
name='pixel',
shape=[1, 28, 28],
data_type='float32',
dtype='float32',
main_program=program)
label = layers.data(
name='label', shape=[1], data_type='int32', main_program=program)
name='label', shape=[1], dtype='int32', main_program=program)
conv_pool_1 = nets.simple_img_conv_pool(
input=images,
filter_size=5,
......@@ -112,39 +109,39 @@ class TestBook(unittest.TestCase):
dict_size = 10000
embed_size = 32
first_word = layers.data(
name='firstw', shape=[1], data_type='int64', main_program=program)
name='firstw', shape=[1], dtype='int64', main_program=program)
second_word = layers.data(
name='secondw', shape=[1], data_type='int64', main_program=program)
name='secondw', shape=[1], dtype='int64', main_program=program)
third_word = layers.data(
name='thirdw', shape=[1], data_type='int64', main_program=program)
name='thirdw', shape=[1], dtype='int64', main_program=program)
forth_word = layers.data(
name='forthw', shape=[1], data_type='int64', main_program=program)
name='forthw', shape=[1], dtype='int64', main_program=program)
next_word = layers.data(
name='nextw', shape=[1], data_type='int64', main_program=program)
name='nextw', shape=[1], dtype='int64', main_program=program)
embed_first = layers.embedding(
input=first_word,
size=[dict_size, embed_size],
data_type='float32',
dtype='float32',
param_attr={'name': 'shared_w'},
main_program=program)
embed_second = layers.embedding(
input=second_word,
size=[dict_size, embed_size],
data_type='float32',
dtype='float32',
param_attr={'name': 'shared_w'},
main_program=program)
embed_third = layers.embedding(
input=third_word,
size=[dict_size, embed_size],
data_type='float32',
dtype='float32',
param_attr={'name': 'shared_w'},
main_program=program)
embed_forth = layers.embedding(
input=forth_word,
size=[dict_size, embed_size],
data_type='float32',
dtype='float32',
param_attr={'name': 'shared_w'},
main_program=program)
......@@ -173,12 +170,9 @@ class TestBook(unittest.TestCase):
# Change g_program, so the rest layers use `g_program`
images = layers.data(
name='pixel',
shape=[784],
data_type='float32',
main_program=program)
name='pixel', shape=[784], dtype='float32', main_program=program)
label = layers.data(
name='label', shape=[1], data_type='int32', main_program=program)
name='label', shape=[1], dtype='int32', main_program=program)
hidden = layers.fc(input=images, size=128, main_program=program)
crf = layers.linear_chain_crf(
input=hidden, label=label, main_program=program)
......
python/paddle/v2/fluid/tests/test_lod_tensor_array_ops.py
浏览文件 @ ce4e0e90
......@@ -132,7 +132,7 @@ class TestCPULoDTensorArrayOpGrad(unittest.TestCase):
x = layers.data(
name='x',
shape=[1],
data_type='float32',
dtype='float32',
main_program=program,
stop_gradient=False)
table = layers.lod_rank_table(x, level=0, main_program=program)
......
python/paddle/v2/fluid/tests/test_mnist_if_else_op.py
浏览文件 @ ce4e0e90
......@@ -11,10 +11,9 @@ import numpy as np
class TestMNISTIfElseOp(unittest.TestCase):
def test_raw_api(self):
kwargs = {'startup_program': Program(), 'main_program': Program()}
image = layers.data(
name='x', shape=[784], data_type='float32', **kwargs)
image = layers.data(name='x', shape=[784], dtype='float32', **kwargs)
label = layers.data(name='y', shape=[1], data_type='int64', **kwargs)
label = layers.data(name='y', shape=[1], dtype='int64', **kwargs)
limit = layers.fill_constant_batch_size_like(
input=label, dtype='int64', shape=[1], value=5.0, **kwargs)
......@@ -84,10 +83,9 @@ class TestMNISTIfElseOp(unittest.TestCase):
def test_ifelse(self):
kwargs = {'startup_program': Program(), 'main_program': Program()}
image = layers.data(
name='x', shape=[784], data_type='float32', **kwargs)
image = layers.data(name='x', shape=[784], dtype='float32', **kwargs)
label = layers.data(name='y', shape=[1], data_type='int64', **kwargs)
label = layers.data(name='y', shape=[1], dtype='int64', **kwargs)
limit = layers.fill_constant_batch_size_like(
input=label, dtype='int64', shape=[1], value=5.0, **kwargs)
......
python/paddle/v2/fluid/tests/test_parameter.py
浏览文件 @ ce4e0e90
......@@ -20,7 +20,7 @@ class TestParameter(unittest.TestCase):
self.assertIsNotNone(param)
self.assertEqual('fc.w', param.name)
self.assertEqual((784, 100), param.shape)
self.assertEqual(core.DataType.FP32, param.data_type)
self.assertEqual(core.DataType.FP32, param.dtype)
self.assertEqual(0, param.block.idx)
exe = Executor(core.CPUPlace())
p = exe.run(g_main_program, fetch_list=[param])[0]
......
python/paddle/v2/fluid/tests/test_protobuf_descs.py
浏览文件 @ ce4e0e90
......@@ -101,13 +101,13 @@ class TestVarDesc(unittest.TestCase):
self.assertEqual(src_shape, res_shape)
self.assertEqual(core.VarDesc.VarType.SELECTED_ROWS, var.type())
def test_data_type(self):
def test_dtype(self):
program_desc = core.ProgramDesc()
block = program_desc.block(0)
var = block.var('my_var')
var.set_type(core.VarDesc.VarType.LOD_TENSOR)
var.set_data_type(core.DataType.INT32)
self.assertEqual(core.DataType.INT32, var.data_type())
var.set_dtype(core.DataType.INT32)
self.assertEqual(core.DataType.INT32, var.dtype())
self.assertEqual(core.VarDesc.VarType.LOD_TENSOR, var.type())
......
python/paddle/v2/fluid/tests/test_recurrent_op.py
浏览文件 @ ce4e0e90
......@@ -118,14 +118,14 @@ class RecurrentOpTest1(unittest.TestCase):
def create_rnn_op(self):
x = layers.data(
shape=[self.sent_len, self.batch_size, self.input_dim],
data_type='float32',
dtype='float32',
name='x',
append_batch_size=False,
**self.p_info)
x.stop_gradient = False
h_boot = layers.data(
shape=[self.input_dim],
data_type='float32',
dtype='float32',
name='h_boot',
**self.p_info)
h_boot.stop_gradient = False
......@@ -251,14 +251,14 @@ class RecurrentOpTest2(RecurrentOpTest1):
def create_rnn_op(self):
x = layers.data(
shape=[self.sent_len, self.batch_size, self.input_dim],
data_type='float32',
dtype='float32',
name='x',
append_batch_size=False,
**self.p_info)
x.stop_gradient = False
h_boot = layers.data(
shape=[self.input_dim],
data_type='float32',
dtype='float32',
name='h_boot',
**self.p_info)
h_boot.stop_gradient = False
......@@ -350,21 +350,21 @@ class RecurrentOpMultipleMemoryTest(RecurrentOpTest1):
def create_rnn_op(self):
x = layers.data(
shape=[self.sent_len, self.batch_size, self.input_dim],
data_type='float32',
dtype='float32',
name='x',
append_batch_size=False,
**self.p_info)
x.stop_gradient = False
h_boot1 = layers.data(
shape=[self.batch_size, self.input_dim],
data_type='float32',
dtype='float32',
name='h_boot1',
append_batch_size=False,
**self.p_info)
h_boot1.stop_gradient = False
h_boot2 = layers.data(
shape=[self.batch_size, self.input_dim],
data_type='float32',
dtype='float32',
name='h_boot2',
append_batch_size=False,
**self.p_info)
......@@ -435,7 +435,7 @@ class RecurrentOpNoMemBootTest(RecurrentOpTest1):
def create_rnn_op(self):
x = layers.data(
shape=[self.sent_len, self.batch_size, self.input_dim],
data_type='float32',
dtype='float32',
name='x',
append_batch_size=False,
**self.p_info)
......
python/paddle/v2/fluid/tests/test_shrink_rnn_memory.py
浏览文件 @ ce4e0e90
......@@ -9,7 +9,7 @@ import numpy
class TestShrinkRNNMemory(unittest.TestCase):
def test_shrink_rnn_memory(self):
x = layers.data('x', shape=[100], data_type='float32')
x = layers.data('x', shape=[100], dtype='float32')
x.stop_gradient = False
table = layers.lod_rank_table(x=x)
i = layers.zeros(dtype='int64', shape=[1])
......
python/paddle/v2/fluid/tests/test_split_and_merge_lod_tensor_op.py
浏览文件 @ ce4e0e90
......@@ -123,13 +123,13 @@ class TestCPUSplitMergeLoDTensorGrad(unittest.TestCase):
x = layers.data(
name='x',
shape=[1],
data_type='float32',
dtype='float32',
main_program=program,
stop_gradient=False)
y = layers.data(
name='y',
shape=[1],
data_type='bool',
dtype='bool',
main_program=program,
stop_gradient=False)
......
python/paddle/v2/fluid/tests/test_variable.py
浏览文件 @ ce4e0e90
......@@ -22,13 +22,13 @@ class TestVariable(unittest.TestCase):
w = b.create_var(
dtype="float64", shape=[784, 100], lod_level=0, name="fc.w")
self.assertNotEqual(str(w), "")
self.assertEqual(core.DataType.FP64, w.data_type)
self.assertEqual(core.DataType.FP64, w.dtype)
self.assertEqual((784, 100), w.shape)
self.assertEqual("fc.w", w.name)
self.assertEqual(0, w.lod_level)
w = b.create_var(name='fc.w')
self.assertEqual(core.DataType.FP64, w.data_type)
self.assertEqual(core.DataType.FP64, w.dtype)
self.assertEqual((784, 100), w.shape)
self.assertEqual("fc.w", w.name)
self.assertEqual(0, w.lod_level)
......
python/paddle/v2/fluid/tests/test_while_op.py
浏览文件 @ ce4e0e90
......@@ -9,11 +9,11 @@ import numpy
class TestWhileOp(unittest.TestCase):
def test_simple_forward(self):
d0 = layers.data(
"d0", shape=[10], append_batch_size=False, data_type='float32')
"d0", shape=[10], append_batch_size=False, dtype='float32')
d1 = layers.data(
"d1", shape=[10], append_batch_size=False, data_type='float32')
"d1", shape=[10], append_batch_size=False, dtype='float32')
d2 = layers.data(
"d2", shape=[10], append_batch_size=False, data_type='float32')
"d2", shape=[10], append_batch_size=False, dtype='float32')
i = layers.zeros(shape=[1], dtype='int64')
i.stop_gradient = True
init = layers.zeros(shape=[10], dtype='float32')
......
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