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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include <glog/logging.h>
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namespace paddle {
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enum ValueType {
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VALUE_TYPE_INT32 = 0,
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VALUE_TYPE_FLOAT = 1,
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VALUE_TYPE_DOUBLE = 2,
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VALUE_TYPE_BYTE = 3
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};
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enum DeviceType {
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DEVICE_TYPE_UNSPECIFIED = 0,
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DEVICE_TYPE_CPU = 1,
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DEVICE_TYPE_GPU = 2
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};
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inline int sizeOfValuType(ValueType valueType) {
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if (valueType == VALUE_TYPE_INT32) {
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return 4;
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} else if (valueType == VALUE_TYPE_FLOAT) {
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return 4;
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} else if (valueType == VALUE_TYPE_DOUBLE) {
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return 8;
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} else {
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LOG(FATAL) << "Unknown type: " << valueType;
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return 0;
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}
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}
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template <typename T>
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struct DataType;
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template <>
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struct DataType<float> {
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static const ValueType value = VALUE_TYPE_FLOAT;
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};
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template <>
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struct DataType<double> {
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static const ValueType value = VALUE_TYPE_DOUBLE;
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};
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/**
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* TensorShape used to represent shape of normal tensor.
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*/
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class TensorShape {
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public:
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TensorShape() : ndims_(0), nelements_(0) { initDims(0); }
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TensorShape(size_t ndims) : ndims_(ndims), nelements_(1) { initDims(ndims); };
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TensorShape(std::initializer_list<size_t> dims) {
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ndims_ = dims.size();
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initDims(ndims_);
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std::copy(dims.begin(), dims.end(), dims_.begin());
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numElements();
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};
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TensorShape(const TensorShape& t)
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: ndims_(t.ndims_), nelements_(t.nelements_) {
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initDims(ndims_);
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std::copy(t.dims_.begin(), t.dims_.end(), dims_.begin());
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};
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// get the size of specified dimension
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size_t operator[](size_t dim) const {
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CHECK_GE(dim, 0);
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CHECK_LT(dim, ndims_);
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return dims_[dim];
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}
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// set the size of specified dimension
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void setDim(size_t dim, size_t size) {
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CHECK_GE(dim, 0);
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CHECK_LT(dim, ndims_);
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dims_[dim] = size;
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numElements();
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}
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// number of dimensions of the tensor
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size_t ndims() const { return ndims_; }
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size_t getElements() const { return nelements_; }
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private:
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// compute number of elements
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void numElements() {
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nelements_ = 1;
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for (size_t n = 0; n < ndims_; n++) {
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nelements_ *= dims_[n];
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}
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}
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// init dims_
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void initDims(size_t ndims) {
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size_t count = ndims < 4 ? 4 : ndims;
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dims_.assign(count, 1);
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}
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// number of dimensions
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// ndims_ may be not equeal dims_.size()
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size_t ndims_;
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// number of elements
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size_t nelements_;
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std::vector<size_t> dims_;
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};
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} // namespace paddle
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@ -0,0 +1,53 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "TensorType.h"
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#include <gtest/gtest.h>
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namespace paddle {
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TEST(TensorShape, Constructor) {
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TensorShape t1;
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EXPECT_EQ(t1.ndims(), 0);
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EXPECT_EQ(t1.getElements(), 0);
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TensorShape t2(3);
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EXPECT_EQ(t2.ndims(), 3);
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EXPECT_EQ(t2.getElements(), 1);
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TensorShape t3({8, 10});
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EXPECT_EQ(t3.ndims(), 2);
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EXPECT_EQ(t3.getElements(), 80);
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TensorShape t4(t3);
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EXPECT_EQ(t4.ndims(), t3.ndims());
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EXPECT_EQ(t4.getElements(), t3.getElements());
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TensorShape t5({1, 2, 3, 4, 5});
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EXPECT_EQ(t5.ndims(), 5);
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EXPECT_EQ(t5.getElements(), 120);
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}
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TEST(TensorShape, GetAndSet) {
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TensorShape t({1, 2, 3});
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EXPECT_EQ(t.ndims(), 3);
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EXPECT_EQ(t.getElements(), 6);
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EXPECT_EQ(t[1], 2);
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t.setDim(1, 100);
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EXPECT_EQ(t.getElements(), 300);
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EXPECT_EQ(t[1], 100);
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}
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} // namespace paddle
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