Fix clang warnings
diff --git a/torch/csrc/autograd/function.cpp b/torch/csrc/autograd/function.cpp
index 61be25b..2c12ca3 100644
--- a/torch/csrc/autograd/function.cpp
+++ b/torch/csrc/autograd/function.cpp
@@ -354,7 +354,7 @@
// the object and remembers which output did we use.
PyObject *prev_fn = input_var->creator ? input_var->creator : (PyObject*)input_var;
Py_INCREF(prev_fn);
- self->previous_functions[i] = std::move(THPFunctionPtr(prev_fn, input_var->output_nr));
+ self->previous_functions[i] = THPFunctionPtr(prev_fn, input_var->output_nr);
}
if (!_mark_dirty(self, t2var))
diff --git a/torch/csrc/generic/Tensor.cpp b/torch/csrc/generic/Tensor.cpp
index 8035b1e..d590a5e 100644
--- a/torch/csrc/generic/Tensor.cpp
+++ b/torch/csrc/generic/Tensor.cpp
@@ -81,6 +81,42 @@
THPUtils_setError(error.c_str());
}
+#ifdef NUMPY_TYPE_ENUM
+THTensor* THPTensor_(fromNumpy)(PyObject *numpy_array) {
+ PyArrayObject *array = (PyArrayObject*)numpy_array;
+ THStoragePtr storage = THStorage_(newWithDataAndAllocator)(
+ (real*)PyArray_DATA(array),
+ PyArray_NBYTES(array) / sizeof(real),
+ &THNumpyArrayAllocator,
+ new NumpyArrayAllocator(numpy_array));
+
+ // Numpy and Torch disagree on empty tensors. In Torch, an empty
+ // tensor is a tensor with zero dimensions. In Numpy, an empty tensor
+ // keeps its shape, but has 0 as the size of one of the dimensions.
+ // So we'll convert all Numpy tensors of 0 elements to empty Torch tensors.
+ if (PyArray_SIZE(array) != 0) {
+ auto ndim = PyArray_NDIM(array);
+ THLongStoragePtr sizes = THLongStorage_newWithSize(ndim);
+ long *sizes_data = sizes->data;
+ for (int i = 0; i < ndim; ++i) {
+ sizes_data[i] = PyArray_DIM(array, i);
+ }
+
+ THLongStoragePtr strides = THLongStorage_newWithSize(ndim);
+ long *strides_data = strides->data;
+ for (int i = 0; i < ndim; ++i) {
+ strides_data[i] = PyArray_STRIDE(array, i) / sizeof(real); // numpy uses bytes, torch uses elements
+ }
+
+ THTensor *result = THTensor_(newWithStorage)(storage, 0, sizes, strides);
+ return result;
+ } else {
+ THTensor *result = THTensor_(newWithStorage)(storage, 0, NULL, NULL);
+ return result;
+ }
+}
+#endif
+
static PyObject * THPTensor_(pynew)(PyTypeObject *type, PyObject *args, PyObject *kwargs)
{
HANDLE_TH_ERRORS
@@ -319,8 +355,8 @@
long dimsize = THTensor_(size)(LIBRARY_STATE TENSOR_VARIABLE, DIM); \
idx = (idx < 0) ? dimsize + idx : idx; \
\
- THPUtils_assert(dimsize > 0, "indexing an empty tensor"); \
- THPUtils_assert(idx >= 0 && idx < dimsize, "index %ld is out of range for " \
+ THPUtils_assertRet(false, dimsize > 0, "indexing an empty tensor"); \
+ THPUtils_assertRet(false, idx >= 0 && idx < dimsize, "index %ld is out of range for " \
"dimension %ld (of size %ld)", idx, DIM, dimsize); \
\
if(THTensor_(nDimension)(LIBRARY_STATE TENSOR_VARIABLE) == 1) { \
@@ -362,7 +398,7 @@
} else if(PyTuple_Check(index)) {
long num_index_dim = (long)PyTuple_Size(index);
long num_tensor_dim = THTensor_(nDimension)(LIBRARY_STATE self->cdata);
- THPUtils_assert(num_index_dim <= num_tensor_dim, "trying to index %ld "
+ THPUtils_assertRet(false, num_index_dim <= num_tensor_dim, "trying to index %ld "
"dimensions of a %ld dimensional tensor", num_index_dim,
num_tensor_dim);
diff --git a/torch/csrc/generic/TensorMethods.cwrap b/torch/csrc/generic/TensorMethods.cwrap
index da66f08..4d4bcd1 100644
--- a/torch/csrc/generic/TensorMethods.cwrap
+++ b/torch/csrc/generic/TensorMethods.cwrap
@@ -154,40 +154,6 @@
return array.release();
}
-
-THTensor* THPTensor_(fromNumpy)(PyObject *numpy_array) {
- PyArrayObject *array = (PyArrayObject*)numpy_array;
- THStoragePtr storage = THStorage_(newWithDataAndAllocator)(
- (real*)PyArray_DATA(array),
- PyArray_NBYTES(array) / sizeof(real),
- &THNumpyArrayAllocator,
- new NumpyArrayAllocator(numpy_array));
-
- // Numpy and Torch disagree on empty tensors. In Torch, an empty
- // tensor is a tensor with zero dimensions. In Numpy, an empty tensor
- // keeps its shape, but has 0 as the size of one of the dimensions.
- // So we'll convert all Numpy tensors of 0 elements to empty Torch tensors.
- if (PyArray_SIZE(array) != 0) {
- auto ndim = PyArray_NDIM(array);
- THLongStoragePtr sizes = THLongStorage_newWithSize(ndim);
- long *sizes_data = sizes->data;
- for (int i = 0; i < ndim; ++i) {
- sizes_data[i] = PyArray_DIM(array, i);
- }
-
- THLongStoragePtr strides = THLongStorage_newWithSize(ndim);
- long *strides_data = strides->data;
- for (int i = 0; i < ndim; ++i) {
- strides_data[i] = PyArray_STRIDE(array, i) / sizeof(real); // numpy uses bytes, torch uses elements
- }
-
- THTensor *result = THTensor_(newWithStorage)(storage, 0, sizes, strides);
- return result;
- } else {
- THTensor *result = THTensor_(newWithStorage)(storage, 0, NULL, NULL);
- return result;
- }
-}
#endif
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