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https://github.com/zebrajr/pytorch.git
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045e7435c3
* Remove remaining TensorTypeUtils functions. Mostly what's remaining is copy utilities -- these are now provided in THCTensorCopy.hpp and templatized on the ScalarType rather than the TensorType. * Have a single THTensor / THCTensor type. As was previously done with Storages, have only a single (dtype-independent) THTensor / THCTensor. For documentation and backwards compatibility purposes, the old names, e.g. TH(Cuda)LongTensor alias the new TH(C)Tensor type. * undef GENERATE_SPARSE.
44 lines
1.2 KiB
C++
44 lines
1.2 KiB
C++
#include "torch/csrc/python_headers.h"
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#include <stdarg.h>
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#include <string>
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#include "THCP.h"
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#include "override_macros.h"
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#define THC_GENERIC_FILE "torch/csrc/generic/utils.cpp"
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#include <THC/THCGenerateAllTypes.h>
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#ifdef WITH_CUDA
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std::vector <THCStream*> THPUtils_PySequence_to_THCStreamList(PyObject *obj) {
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if (!PySequence_Check(obj)) {
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throw std::runtime_error("Expected a sequence in THPUtils_PySequence_to_THCStreamList");
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}
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THPObjectPtr seq = THPObjectPtr(PySequence_Fast(obj, NULL));
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if (seq.get() == NULL) {
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throw std::runtime_error("expected PySequence, but got " + std::string(THPUtils_typename(obj)));
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}
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std::vector<THCStream*> streams;
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Py_ssize_t length = PySequence_Fast_GET_SIZE(seq.get());
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for (Py_ssize_t i = 0; i < length; i++) {
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PyObject *stream = PySequence_Fast_GET_ITEM(seq.get(), i);
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if (PyObject_IsInstance(stream, THCPStreamClass)) {
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streams.push_back( ((THCPStream *)stream)->cdata);
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} else if (stream == Py_None) {
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streams.push_back(NULL);
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} else {
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std::runtime_error("Unknown data type found in stream list. Need THCStream or None");
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}
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}
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return streams;
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}
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template<>
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void THPPointer<THCTensor>::free() {
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if (ptr)
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THCTensor_free(LIBRARY_STATE ptr);
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}
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#endif
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