implement convert_single_elem_array_to_scalar() function (#765)

Author: shssf

dpnp/dpnp_iface.py

@@ -54,6 +54,7 @@
 __all__ = [
     "array_equal",
     "asnumpy",
+    "convert_single_elem_array_to_scalar",
     "dpnp_queue_initialize",
     "dpnp_queue_is_cpu",
     "get_dpnp_descriptor",
@@ -134,6 +135,16 @@ def asnumpy(input, order='C'):
     return numpy.asarray(input, order=order)
 
 
+def convert_single_elem_array_to_scalar(obj):
+    """
+    Convert array with single element to scalar
+    """
+
+    if obj.shape == (1,): # TODO handle shapes like (1,1,1,1)
+        return obj.dtype.type(obj[0])
+
+    return obj
+
 def get_dpnp_descriptor(ext_obj):
     """
     Return True:

dpnp/dpnp_iface_counting.py

@@ -40,14 +40,15 @@
 """
 
 
-import numpy
-
 from dpnp.dpnp_algo.dpnp_algo import *  # TODO need to investigate why dpnp.dpnp_algo can not be used
 from dpnp.dparray import dparray
 
 # full module name because dpnp_iface_counting loaded from cython too early
 from dpnp.dpnp_utils.dpnp_algo_utils import *
 
+import dpnp
+import numpy
+
 __all__ = [
     'count_nonzero'
 ]
@@ -84,11 +85,8 @@ def count_nonzero(in_array1, axis=None, *, keepdims=False):
         if keepdims is not False:
             checker_throw_value_error("count_nonzero", "keepdims", keepdims, False)
 
-        result = dpnp_count_nonzero(in_array1)
-
-        # scalar returned
-        if result.shape == (1,):
-            return result.dtype.type(result[0])
+        result_obj = dpnp_count_nonzero(in_array1)
+        result = dpnp.convert_single_elem_array_to_scalar(result_obj)
 
         return result
 

dpnp/dpnp_iface_linearalgebra.py

@@ -98,11 +98,8 @@ def dot(x1, x2, **kwargs):
         dim2 = x2_desc.ndim
 
         if not (dim1 >= 2 and dim2 == 1) and not (dim1 >= 2 and dim2 >= 2) and (x1_desc.dtype == x2_desc.dtype):
-            result = dpnp_dot(x1_desc, x2_desc)
-
-            # scalar returned
-            if result.shape == (1,):
-                return result.dtype.type(result[0])
+            result_obj = dpnp_dot(x1_desc, x2_desc)
+            result = dpnp.convert_single_elem_array_to_scalar(result_obj)
 
             return result
 

dpnp/dpnp_iface_searching.py

@@ -40,12 +40,13 @@
 """
 
 
-import numpy
-
 from dpnp.dpnp_algo import *
 from dpnp.dparray import dparray
 from dpnp.dpnp_utils import *
 
+import dpnp
+import numpy
+
 
 __all__ = [
     'argmax',
@@ -101,11 +102,8 @@ def argmax(in_array1, axis=None, out=None):
         if out is not None:
             checker_throw_value_error("argmax", "out", type(out), None)
 
-        result = dpnp_argmax(in_array1)
-
-        # scalar returned
-        if result.shape == (1,):
-            return result.dtype.type(result[0])
+        result_obj = dpnp_argmax(in_array1)
+        result = dpnp.convert_single_elem_array_to_scalar(result_obj)
 
         return result
 
@@ -160,11 +158,8 @@ def argmin(in_array1, axis=None, out=None):
         if out is not None:
             checker_throw_value_error("argmin", "out", type(out), None)
 
-        result = dpnp_argmin(in_array1)
-
-        # scalar returned
-        if result.shape == (1,):
-            return result.dtype.type(result[0])
+        result_obj = dpnp_argmin(in_array1)
+        result = dpnp.convert_single_elem_array_to_scalar(result_obj)
 
         return result
 

dpnp/dpnp_iface_statistics.py

@@ -162,13 +162,10 @@ def average(a, axis=None, weights=None, returned=False):
         elif returned:
             pass
         else:
-            array_avg = dpnp_average(a)
+            result_obj = dpnp_average(a)
+            result = dpnp.convert_single_elem_array_to_scalar(result_obj)
 
-            # scalar returned
-            if array_avg.shape == (1,):
-                return array_avg.dtype.type(array_avg[0])
-
-            return array_avg
+            return result
 
     return call_origin(numpy.average, a, axis, weights, returned)
 
@@ -335,11 +332,8 @@ def max(input, axis=None, out=None, keepdims=numpy._NoValue, initial=numpy._NoVa
         elif where is not numpy._NoValue:
             pass
         else:
-            result = dpnp_max(input, axis=axis)
-
-            # scalar returned
-            if result.shape == (1,):
-                return result.dtype.type(result[0])
+            result_obj = dpnp_max(input, axis=axis)
+            result = dpnp.convert_single_elem_array_to_scalar(result_obj)
 
             return result
 
@@ -386,11 +380,8 @@ def mean(a, axis=None, **kwargs):
         elif a.size == 0:
             pass
         else:
-            result = dpnp_mean(a, axis=axis)
-
-            # scalar returned
-            if result.shape == (1,):
-                return result.dtype.type(result[0])
+            result_obj = dpnp_mean(a, axis=axis)
+            result = dpnp.convert_single_elem_array_to_scalar(result_obj)
 
             return result
 
@@ -439,11 +430,8 @@ def median(a, axis=None, out=None, overwrite_input=False, keepdims=False):
         elif keepdims:
             pass
         else:
-            result = dpnp_median(a)
-
-            # scalar returned
-            if result.shape == (1,):
-                return result.dtype.type(result[0])
+            result_obj = dpnp_median(a)
+            result = dpnp.convert_single_elem_array_to_scalar(result_obj)
 
             return result
 
@@ -486,11 +474,8 @@ def min(input, axis=None, out=None, keepdims=numpy._NoValue, initial=numpy._NoVa
         elif where is not numpy._NoValue:
             pass
         else:
-            result = dpnp_min(input, axis=axis)
-
-            # scalar returned
-            if result.shape == (1,):
-                return result.dtype.type(result[0])
+            result_obj = dpnp_min(input, axis=axis)
+            result = dpnp.convert_single_elem_array_to_scalar(result_obj)
 
             return result
 
@@ -524,11 +509,8 @@ def nanvar(arr, axis=None, dtype=None, out=None, ddof=0, keepdims=numpy._NoValue
         elif keepdims is not numpy._NoValue:
             pass
         else:
-            result = dpnp_nanvar(arr, ddof)
-
-            # scalar returned
-            if result.shape == (1,):
-                return result.dtype.type(result[0])
+            result_obj = dpnp_nanvar(arr, ddof)
+            result = dpnp.convert_single_elem_array_to_scalar(result_obj)
 
             return result
 
@@ -586,9 +568,8 @@ def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=numpy._NoValue):
         elif keepdims is not numpy._NoValue:
             pass
         else:
-            result = dpnp_std(a, ddof)
-            if result.shape == (1,):
-                return result.dtype.type(result[0])
+            result_obj = dpnp_std(a, ddof)
+            result = dpnp.convert_single_elem_array_to_scalar(result_obj)
 
             return result
 
@@ -646,9 +627,8 @@ def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=numpy._NoValue):
         elif keepdims is not numpy._NoValue:
             pass
         else:
-            result = dpnp_var(a, ddof)
-            if result.shape == (1,):
-                return result.dtype.type(result[0])
+            result_obj = dpnp_var(a, ddof)
+            result = dpnp.convert_single_elem_array_to_scalar(result_obj)
 
             return result
 

dpnp/linalg/dpnp_iface_linalg.py

@@ -146,11 +146,8 @@ def det(input):
 
     if not use_origin_backend(input) and is_input_dparray:
         if input.shape[-1] == input.shape[-2]:
-            result = dpnp_det(input)
-
-            # scalar returned
-            if result.shape == (1,):
-                return result.dtype.type(result[0])
+            result_obj = dpnp_det(input)
+            result = dpnp.convert_single_elem_array_to_scalar(result_obj)
 
             return result
 
@@ -301,11 +298,8 @@ def matrix_rank(input, tol=None, hermitian=False):
         if hermitian is not False:
             checker_throw_value_error("matrix_rank", "hermitian", hermitian, False)
 
-        result = dpnp_matrix_rank(input)
-
-        # scalar returned
-        if result.shape == (1,):
-            return result.dtype.type(result[0])
+        result_obj = dpnp_matrix_rank(input)
+        result = dpnp.convert_single_elem_array_to_scalar(result_obj)
 
         return result
 
@@ -392,11 +386,8 @@ def norm(input, ord=None, axis=None, keepdims=False):
         elif ord not in [None, 0, 3, 'fro', 'f']:
             pass
         else:
-            result = dpnp_norm(input, ord=ord, axis=axis)
-
-            # scalar returned
-            if result.shape == (1,) and axis is None:
-                return result.dtype.type(result[0])
+            result_obj = dpnp_norm(input, ord=ord, axis=axis)
+            result = dpnp.convert_single_elem_array_to_scalar(result_obj)
 
             return result