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Merge pull request #2928 from reyoung/feature/is_in_gpu
Read/Write a Tensor Python
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/* 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. */ | ||
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#pragma once | ||
#include <paddle/framework/tensor.h> | ||
#include <pybind11/numpy.h> | ||
#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> | ||
struct CastToPyBufferImpl; | ||
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template <size_t I, typename... ARGS> | ||
struct CastToPyBufferImpl<false, I, ARGS...> { | ||
py::buffer_info operator()(framework::Tensor &tensor) { | ||
PADDLE_THROW("This type of tensor cannot be expose to Python"); | ||
return py::buffer_info(); | ||
} | ||
}; | ||
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template <size_t I, typename... ARGS> | ||
struct CastToPyBufferImpl<true, I, ARGS...> { | ||
using CUR_TYPE = typename std::tuple_element<I, std::tuple<ARGS...>>::type; | ||
py::buffer_info operator()(framework::Tensor &tensor) { | ||
PADDLE_ENFORCE(paddle::platform::is_cpu_place(tensor.holder_->place()), | ||
"Only CPU tensor can cast to numpy array"); | ||
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if (std::type_index(typeid(CUR_TYPE)) == tensor.holder_->type()) { | ||
auto dim_vec = framework::vectorize(tensor.dims()); | ||
std::vector<size_t> dims_outside; | ||
std::vector<size_t> strides; | ||
dims_outside.resize(dim_vec.size()); | ||
strides.resize(dim_vec.size()); | ||
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size_t prod = 1; | ||
for (size_t i = dim_vec.size(); i != 0; --i) { | ||
dims_outside[i - 1] = (size_t)dim_vec[i - 1]; | ||
strides[i - 1] = sizeof(CUR_TYPE) * prod; | ||
prod *= dims_outside[i - 1]; | ||
} | ||
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return py::buffer_info( | ||
tensor.mutable_data<CUR_TYPE>(tensor.holder_->place()), | ||
sizeof(CUR_TYPE), | ||
py::format_descriptor<CUR_TYPE>::format(), | ||
(size_t)framework::arity(tensor.dims()), | ||
dims_outside, | ||
strides); | ||
} else { | ||
constexpr bool less = I + 1 < std::tuple_size<std::tuple<ARGS...>>::value; | ||
return CastToPyBufferImpl<less, I + 1, ARGS...>()(tensor); | ||
} | ||
} | ||
}; | ||
} // namespace details | ||
inline py::buffer_info CastToPyBuffer(framework::Tensor &tensor) { | ||
auto buffer_info = details::CastToPyBufferImpl<true, 0, float, int>()(tensor); | ||
return buffer_info; | ||
} | ||
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template <typename T> | ||
void PyTensorSetFromArray( | ||
framework::Tensor &self, | ||
py::array_t<T, py::array::c_style | py::array::forcecast> array) { | ||
std::vector<int> dims; | ||
dims.reserve(array.ndim()); | ||
for (size_t i = 0; i < array.ndim(); ++i) { | ||
dims.push_back((int)array.shape()[i]); | ||
} | ||
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self.set_dims(framework::make_ddim(dims)); | ||
auto *dst = self.mutable_data<T>(paddle::platform::CPUPlace()); | ||
std::memcpy(dst, array.data(), sizeof(T) * array.size()); | ||
} | ||
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} // namespace pybind | ||
} // namespace paddle |
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add_python_test(test_framework test_protobuf.py test_scope.py | ||
test_default_scope_funcs.py test_op_creation_methods.py) | ||
test_default_scope_funcs.py test_op_creation_methods.py | ||
test_tensor.py) |
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import paddle.v2.framework.core as core | ||
import unittest | ||
import numpy | ||
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class TestScope(unittest.TestCase): | ||
def test_int_tensor(self): | ||
scope = core.Scope(None) | ||
var = scope.create_var("test_tensor") | ||
tensor = var.get_tensor() | ||
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tensor.set_dims([1000, 784]) | ||
tensor.alloc_int() | ||
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tensor_array = numpy.array(tensor) | ||
self.assertEqual((1000, 784), tensor_array.shape) | ||
tensor_array[3, 9] = 1 | ||
tensor_array[19, 11] = 2 | ||
tensor.set(tensor_array) | ||
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tensor_array_2 = numpy.array(tensor) | ||
self.assertEqual(1.0, tensor_array_2[3, 9]) | ||
self.assertEqual(2.0, tensor_array_2[19, 11]) | ||
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def test_float_tensor(self): | ||
scope = core.Scope(None) | ||
var = scope.create_var("test_tensor") | ||
tensor = var.get_tensor() | ||
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tensor.set_dims([1000, 784]) | ||
tensor.alloc_float() | ||
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tensor_array = numpy.array(tensor) | ||
self.assertEqual((1000, 784), tensor_array.shape) | ||
tensor_array[3, 9] = 1.0 | ||
tensor_array[19, 11] = 2.0 | ||
tensor.set(tensor_array) | ||
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tensor_array_2 = numpy.array(tensor) | ||
self.assertAlmostEqual(1.0, tensor_array_2[3, 9]) | ||
self.assertAlmostEqual(2.0, tensor_array_2[19, 11]) | ||
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if __name__ == '__main__': | ||
unittest.main() |