fix full and full_like when input is boolean

python/mxnet/ndarray/numpy/_op.py

@@ -366,6 +366,9 @@ def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None): # pylin
  else:
  ret = broadcast_to(fill_value, shape).astype(dtype)
  return ret
+ if isinstance(fill_value, bool):
+ fill_value = int(fill_value)
+ dtype = _np.bool if dtype is None else dtype
  dtype = _np.float32 if dtype is None else dtype
  return _npi.full(shape=shape, value=fill_value, ctx=ctx, dtype=dtype, out=out)
 # pylint: enable=too-many-arguments, redefined-outer-name
@@ -426,6 +429,8 @@ def full_like(a, fill_value, dtype=None, order='C', ctx=None, out=None): # pylin
  raise NotImplementedError
  if ctx is None:
  ctx = current_context()
+ if isinstance(fill_value, bool):
+ fill_value = int(fill_value)
  return _npi.full_like(a, fill_value=fill_value, dtype=dtype, ctx=ctx, out=out)
 
 

python/mxnet/symbol/numpy/_symbol.py

@@ -1255,6 +1255,9 @@ def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None): # pylin
  else:
  ret = broadcast_to(fill_value, shape).astype(dtype)
  return ret
+ if isinstance(fill_value, bool):
+ fill_value = int(fill_value)
+ dtype = _np.bool if dtype is None else dtype
  dtype = _np.float32 if dtype is None else dtype
  return _npi.full(shape=shape, value=fill_value, ctx=ctx, dtype=dtype, out=out)
 
@@ -1299,6 +1302,8 @@ def full_like(a, fill_value, dtype=None, order='C', ctx=None, out=None): # pyli
  raise NotImplementedError
  if ctx is None:
  ctx = current_context()
+ if isinstance(fill_value, bool):
+ fill_value = int(fill_value)
  return _npi.full_like(a, fill_value=fill_value, ctx=ctx, dtype=dtype, out=out)
 
 

src/operator/tensor/init_op.h

@@ -86,14 +86,15 @@ struct FullLikeOpParam : public dmlc::Parameter<FullLikeOpParam> {
  dmlc::optional<int> dtype;
  DMLC_DECLARE_PARAMETER(FullLikeOpParam) {
  DMLC_DECLARE_FIELD(fill_value)
- .describe("Value with which to fill newly created tensor");
+  .describe("Value with which to fill newly created tensor");
  DMLC_DECLARE_FIELD(ctx)
- .set_default("")
- .describe("Context of output, in format [cpu|gpu|cpu_pinned](n)."
- "Only used for imperative calls.");
- DMLC_DECLARE_FIELD(dtype).set_default(dmlc::optional<int>())
- MXNET_ADD_ALL_TYPES
- .describe("Target data type.");
+ .set_default("")
+ .describe("Context of output, in format [cpu|gpu|cpu_pinned](n)."
+ "Only used for imperative calls.");
+ DMLC_DECLARE_FIELD(dtype)
+ .set_default(dmlc::optional<int>())
+ MXNET_ADD_ALL_TYPES_WITH_BOOL
+ .describe("Target data type.");
  }
 };
 

tests/python/unittest/test_numpy_op.py

@@ -5881,6 +5881,8 @@ def hybrid_forward(self, F, a):
  configs = [
  ((3, 4), 2.0),
  ((0, 3), 2.0),
+ ((2, 3), True),
+ ((3, 0), False),
  ((3, 4), np.array(2.0)),
  ((0, 3), np.array(2.0)),
  ((2, 3), np.array([1, 2, 3], dtype=np.float32)),
@@ -5890,7 +5892,7 @@ def hybrid_forward(self, F, a):
  ]
 
  rtol, atol = 1e-3, 1e-5
- dtypes = ['float16', 'float32', 'float64', 'int8', 'int32', 'int64']
+ dtypes = ['float16', 'float32', 'float64', 'int8', 'int32', 'int64', 'bool']
  for shape, fill_value in configs:
  for hybridize in [True, False]:
  for dtype in dtypes:
@@ -5931,7 +5933,7 @@ def hybrid_forward(self, F, x, *args, **kwargs):
  if StrictVersion(platform.python_version()) < StrictVersion('3.0.0'):
  return
 
- dtypes = ['float64', 'float32', 'float16', 'int64', 'int32', 'int8']
+ dtypes = ['float64', 'float32', 'float16', 'int64', 'int32', 'int8', 'bool']
  shapes = [
  (),
  (1,),
@@ -5944,11 +5946,11 @@ def hybrid_forward(self, F, x, *args, **kwargs):
  (3, 3, 1, 0),
  ]
  # numpy.full_like operator in py2 cannot handle shape like (5, 0, 3) properly
- fill_values = [0, 1, 2, 3, 4, 5, 6]
+ fill_values = [0, 1, 2, 3, 4, 5, 6, True, False]
  flags = [True, False]
  for fill_value, dtype, shape, hybridize in itertools.product(
  fill_values, dtypes, shapes, flags):
- param_dtype= _np.random.choice(dtypes)
+ param_dtype = _np.random.choice(dtypes)
  a = np.random.uniform(low=0, high=100, size=shape, dtype='float64').astype(dtype)
  test = TestFullLike(fill_value, param_dtype, npx.current_context())
  expected_ret = _np.full_like(a.asnumpy(), fill_value=fill_value, dtype=param_dtype)