提交 3e812383 编写于 作者: Y Yu Yang

Merge branch 'develop' of github.com:baidu/Paddle into gradient_check_utils

......@@ -17,10 +17,14 @@
- id: detect-private-key
files: (?!.*third_party)^.*$ | (?!.*book)^.*$
- id: end-of-file-fixer
- repo: https://github.com/PaddlePaddle/clang-format-pre-commit-hook.git
sha: 28c0ea8a67a3e2dbbf4822ef44e85b63a0080a29
- repo: local
hooks:
- id: clang-formater
- id: clang-format
name: clang-format
description: Format files with ClangFormat.
entry: clang-format -i
language: system
files: \.(c|cc|cxx|cpp|h|hpp|hxx)$
- repo: https://github.com/PaddlePaddle/pre-commit-golang
sha: 8337620115c25ff8333f1b1a493bd031049bd7c0
hooks:
......
......@@ -44,19 +44,26 @@ endfunction()
op_library(add_op SRCS add_op.cc add_op.cu)
cc_test(add_op_test SRCS add_op_test.cc DEPS add_op)
op_library(mean_op SRCS mean_op.cc mean_op.cu)
cc_test(mean_op_test SRCS mean_op_test.cc DEPS mean_op)
op_library(mul_op SRCS mul_op.cc mul_op.cu)
op_library(rowwise_add_op SRCS rowwise_add_op.cu rowwise_add_op.cc)
op_library(sigmoid_op SRCS sigmoid_op.cu sigmoid_op.cc)
op_library(sigmoid_op SRCS sigmoid_op.cc sigmoid_op.cu)
op_library(softmax_op SRCS softmax_op.cc softmax_op.cu)
op_library(cross_entropy_op SRCS cross_entropy_op.cc cross_entropy_op.cu)
op_library(fill_zeros_like_op SRCS fill_zeros_like_op.cc fill_zeros_like_op.cu)
op_library(fc_op SRCS fc_op.cc DEPS mul_op rowwise_add_op sigmoid_op
softmax_op net)
op_library(sgd_op SRCS sgd_op.cc sgd_op.cu)
op_library(recurrent_network_op SRCS recurrent_network_op.cc DEPS op_desc
tensor op_registry operator net)
cc_test(recurrent_network_op_test SRCS recurrent_network_op_test.cc DEPS
recurrent_network_op gtest mul_op add_op)
op_library(fc_op
SRCS fc_op.cc
DEPS mul_op rowwise_add_op sigmoid_op softmax_op net)
op_library(recurrent_network_op
SRCS recurrent_network_op.cc
DEPS op_desc tensor net)
cc_test(recurrent_network_op_test
SRCS recurrent_network_op_test.cc
DEPS recurrent_network_op mul_op add_op)
/* 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/mean_op.h"
namespace paddle {
namespace operators {
class MeanOp : public OperatorWithKernel {
protected:
void InferShape(const InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 1, "Input size of AddOp must be one");
PADDLE_ENFORCE(ctx.OutputSize() == 1, "Output size of AddOp must be one");
PADDLE_ENFORCE(ctx.InputVar(0) != nullptr && ctx.OutputVar(0) != nullptr,
"Input/Output of MeanOp must be initialized.");
ctx.Output<Tensor>(0)->Resize(framework::make_ddim({1}));
}
};
class MeanOpMaker : public OpProtoAndCheckerMaker {
public:
MeanOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input of mean op");
AddOutput("Out", "The output of mean op");
AddComment("Mean Operator");
}
};
} // namespace operators
} // namespace paddle
REGISTER_OP(mean, ops::MeanOp, ops::MeanOpMaker);
REGISTER_OP_CPU_KERNEL(mean, ops::MeanKernel<ops::CPUPlace, float>);
#define EIGEN_USE_GPU
#include "paddle/operators/mean_op.h"
REGISTER_OP_GPU_KERNEL(mean, ops::MeanKernel<ops::GPUPlace, float>);
/* 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/operators/type_alias.h"
namespace paddle {
namespace operators {
template <typename Place, typename T>
class MeanKernel : public OpKernel {
public:
void Compute(const ExecutionContext& context) const override {
auto input = context.Input<Tensor>(0);
auto output = context.Output<Tensor>(0);
output->mutable_data<T>(context.GetPlace());
EigenScalar<T>::From(*output).device(*(context.GetEigenDevice<Place>())) =
EigenVector<T>::Flatten(*input).mean();
}
};
} // namespace operators
} // namespace paddle
/* 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 <gtest/gtest.h>
#include <paddle/framework/op_registry.h>
USE_OP(mean);
TEST(MeanOp, GetOpProto) {
auto& protos = paddle::framework::OpRegistry::protos();
auto it = protos.find("mean");
ASSERT_NE(it, protos.end());
}
cc_library(paddle_pybind SHARED SRCS pybind.cc DEPS pybind python
add_op fc_op sgd_op cross_entropy_op recurrent_network_op)
cc_library(paddle_pybind SHARED
SRCS pybind.cc
DEPS pybind python
fc_op
sgd_op
add_op
mean_op
cross_entropy_op
recurrent_network_op)
......@@ -33,6 +33,7 @@ USE_OP(onehot_cross_entropy);
USE_OP_WITHOUT_KERNEL(fc);
USE_OP(sgd);
USE_OP(mul);
USE_OP(mean);
USE_OP(sigmoid);
USE_OP(softmax);
USE_OP(rowwise_add);
......
......@@ -10,6 +10,7 @@ add_python_test(test_framework
test_sgd_op.py
test_cross_entropy_op.py
test_mul_op.py
test_mean_op.py
test_sigmoid_op.py
test_softmax_op.py
test_rowwise_add_op.py
......
import unittest
from op_test_util import OpTestMeta
import numpy as np
class TestMeanOp(unittest.TestCase):
__metaclass__ = OpTestMeta
def setUp(self):
self.type = "mean"
self.X = np.random.random((32, 784)).astype("float32")
self.Out = np.mean(self.X)
if __name__ == '__main__':
unittest.main()
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