Yu Yang
8 years ago
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GitHub
commit
c85a323672
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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 <paddle/framework/tensor.h>
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#include <pybind11/numpy.h>
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#include <pybind11/pybind11.h>
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namespace py = pybind11;
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namespace paddle {
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namespace pybind {
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namespace details {
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template <bool less, size_t I, typename... ARGS>
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struct CastToPyBufferImpl;
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template <size_t I, typename... ARGS>
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struct CastToPyBufferImpl<false, I, ARGS...> {
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py::buffer_info operator()(framework::Tensor &tensor) {
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PADDLE_THROW("This type of tensor cannot be expose to Python");
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return py::buffer_info();
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}
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};
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template <size_t I, typename... ARGS>
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struct CastToPyBufferImpl<true, I, ARGS...> {
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using CUR_TYPE = typename std::tuple_element<I, std::tuple<ARGS...>>::type;
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py::buffer_info operator()(framework::Tensor &tensor) {
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PADDLE_ENFORCE(paddle::platform::is_cpu_place(tensor.holder_->place()),
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"Only CPU tensor can cast to numpy array");
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if (std::type_index(typeid(CUR_TYPE)) == tensor.holder_->type()) {
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auto dim_vec = framework::vectorize(tensor.dims());
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std::vector<size_t> dims_outside;
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std::vector<size_t> strides;
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dims_outside.resize(dim_vec.size());
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strides.resize(dim_vec.size());
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size_t prod = 1;
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for (size_t i = dim_vec.size(); i != 0; --i) {
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dims_outside[i - 1] = (size_t)dim_vec[i - 1];
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strides[i - 1] = sizeof(CUR_TYPE) * prod;
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prod *= dims_outside[i - 1];
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}
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return py::buffer_info(
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tensor.mutable_data<CUR_TYPE>(tensor.holder_->place()),
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sizeof(CUR_TYPE),
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py::format_descriptor<CUR_TYPE>::format(),
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(size_t)framework::arity(tensor.dims()),
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dims_outside,
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strides);
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} else {
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constexpr bool less = I + 1 < std::tuple_size<std::tuple<ARGS...>>::value;
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return CastToPyBufferImpl<less, I + 1, ARGS...>()(tensor);
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}
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}
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};
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} // namespace details
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inline py::buffer_info CastToPyBuffer(framework::Tensor &tensor) {
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auto buffer_info = details::CastToPyBufferImpl<true, 0, float, int>()(tensor);
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return buffer_info;
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}
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template <typename T>
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void PyTensorSetFromArray(
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framework::Tensor &self,
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py::array_t<T, py::array::c_style | py::array::forcecast> array) {
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std::vector<int> dims;
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dims.reserve(array.ndim());
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for (size_t i = 0; i < array.ndim(); ++i) {
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dims.push_back((int)array.shape()[i]);
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}
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self.set_dims(framework::make_ddim(dims));
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auto *dst = self.mutable_data<T>(paddle::platform::CPUPlace());
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std::memcpy(dst, array.data(), sizeof(T) * array.size());
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}
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} // namespace pybind
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} // namespace paddle
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@ -1,2 +1,3 @@
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add_python_test(test_framework test_protobuf.py test_scope.py
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test_default_scope_funcs.py test_op_creation_methods.py)
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test_default_scope_funcs.py test_op_creation_methods.py
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test_tensor.py)
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@ -0,0 +1,45 @@
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import paddle.v2.framework.core as core
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import unittest
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import numpy
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class TestScope(unittest.TestCase):
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def test_int_tensor(self):
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scope = core.Scope(None)
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var = scope.create_var("test_tensor")
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tensor = var.get_tensor()
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tensor.set_dims([1000, 784])
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tensor.alloc_int()
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tensor_array = numpy.array(tensor)
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self.assertEqual((1000, 784), tensor_array.shape)
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tensor_array[3, 9] = 1
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tensor_array[19, 11] = 2
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tensor.set(tensor_array)
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tensor_array_2 = numpy.array(tensor)
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self.assertEqual(1.0, tensor_array_2[3, 9])
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self.assertEqual(2.0, tensor_array_2[19, 11])
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def test_float_tensor(self):
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scope = core.Scope(None)
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var = scope.create_var("test_tensor")
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tensor = var.get_tensor()
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tensor.set_dims([1000, 784])
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tensor.alloc_float()
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tensor_array = numpy.array(tensor)
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self.assertEqual((1000, 784), tensor_array.shape)
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tensor_array[3, 9] = 1.0
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tensor_array[19, 11] = 2.0
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tensor.set(tensor_array)
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tensor_array_2 = numpy.array(tensor)
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self.assertAlmostEqual(1.0, tensor_array_2[3, 9])
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self.assertAlmostEqual(2.0, tensor_array_2[19, 11])
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if __name__ == '__main__':
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unittest.main()
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