experiments/STT-tensorflow
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Replace deprecated usage of np.asscalar with np.ndarray.item()

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[`numpy.asscalar()` is deprecated since version 1.16](https://github.com/numpy/numpy/blob/master/numpy/lib/type_check.py#L519-L548).

This PR replaces its usage with [`numpy.ndarray.item()`](https://www.numpy.org/devdocs/reference/generated/numpy.ndarray.item.html)
This commit is contained in:
Lukas Geiger 2019-01-24 13:59:46 +01:00
parent 54dc5baa26
commit 3623f9ae51
5 changed files with 17 additions and 17 deletions
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2
tensorflow/compiler/xla/python_api/xla_literal.py
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@ -69,7 +69,7 @@ def _ConvertNumpyArrayToLiteral(ndarray):
if ndarray.ndim == 0: if ndarray.ndim == 0:
getattr(literal, type_record.literal_field_name).append( getattr(literal, type_record.literal_field_name).append(
_np.asscalar(ndarray.astype(type_record.literal_field_type))) ndarray.astype(type_record.literal_field_type).item())
else: else:
# Ndarrays with boolean dtypes need special type conversion with protobufs # Ndarrays with boolean dtypes need special type conversion with protobufs
if ndarray.dtype in {_np.bool_, _np.dtype('bool')}: if ndarray.dtype in {_np.bool_, _np.dtype('bool')}:

2
tensorflow/python/framework/fast_tensor_util.pyx
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@ -131,4 +131,4 @@ def AppendBoolArrayToTensorProto(tensor_proto, nparray):
cdef long i, n cdef long i, n
n = nparray.size n = nparray.size
for i in range(n): for i in range(n):
tensor_proto.bool_val.append(np.asscalar(nparray[i])) tensor_proto.bool_val.append(nparray.item(i))

26
tensorflow/python/framework/tensor_util.py
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@ -42,7 +42,7 @@ from tensorflow.python.util.tf_export import tf_export
def ExtractBitsFromFloat16(x): def ExtractBitsFromFloat16(x):
return np.asscalar(np.asarray(x, dtype=np.float16).view(np.uint16)) return np.asarray(x, dtype=np.float16).view(np.uint16).item()
def SlowAppendFloat16ArrayToTensorProto(tensor_proto, proto_values): def SlowAppendFloat16ArrayToTensorProto(tensor_proto, proto_values):
@ -58,8 +58,8 @@ def _MediumAppendFloat16ArrayToTensorProto(tensor_proto, proto_values):
def ExtractBitsFromBFloat16(x): def ExtractBitsFromBFloat16(x):
return np.asscalar( return np.asarray(
np.asarray(x, dtype=dtypes.bfloat16.as_numpy_dtype).view(np.uint16)) x, dtype=dtypes.bfloat16.as_numpy_dtype).view(np.uint16).item()
def SlowAppendBFloat16ArrayToTensorProto(tensor_proto, proto_values): def SlowAppendBFloat16ArrayToTensorProto(tensor_proto, proto_values):
@ -122,39 +122,39 @@ if _FAST_TENSOR_UTIL_AVAILABLE:
else: else:
def SlowAppendFloat32ArrayToTensorProto(tensor_proto, proto_values): def SlowAppendFloat32ArrayToTensorProto(tensor_proto, proto_values):
tensor_proto.float_val.extend([np.asscalar(x) for x in proto_values]) tensor_proto.float_val.extend([x.item() for x in proto_values])
def SlowAppendFloat64ArrayToTensorProto(tensor_proto, proto_values): def SlowAppendFloat64ArrayToTensorProto(tensor_proto, proto_values):
tensor_proto.double_val.extend([np.asscalar(x) for x in proto_values]) tensor_proto.double_val.extend([x.item() for x in proto_values])
def SlowAppendIntArrayToTensorProto(tensor_proto, proto_values): def SlowAppendIntArrayToTensorProto(tensor_proto, proto_values):
tensor_proto.int_val.extend([np.asscalar(x) for x in proto_values]) tensor_proto.int_val.extend([x.item() for x in proto_values])
def SlowAppendInt64ArrayToTensorProto(tensor_proto, proto_values): def SlowAppendInt64ArrayToTensorProto(tensor_proto, proto_values):
tensor_proto.int64_val.extend([np.asscalar(x) for x in proto_values]) tensor_proto.int64_val.extend([x.item() for x in proto_values])
def SlowAppendQIntArrayToTensorProto(tensor_proto, proto_values): def SlowAppendQIntArrayToTensorProto(tensor_proto, proto_values):
tensor_proto.int_val.extend([np.asscalar(x[0]) for x in proto_values]) tensor_proto.int_val.extend([x.item(0) for x in proto_values])
def SlowAppendUInt32ArrayToTensorProto(tensor_proto, proto_values): def SlowAppendUInt32ArrayToTensorProto(tensor_proto, proto_values):
tensor_proto.uint32_val.extend([np.asscalar(x) for x in proto_values]) tensor_proto.uint32_val.extend([x.item() for x in proto_values])
def SlowAppendUInt64ArrayToTensorProto(tensor_proto, proto_values): def SlowAppendUInt64ArrayToTensorProto(tensor_proto, proto_values):
tensor_proto.uint64_val.extend([np.asscalar(x) for x in proto_values]) tensor_proto.uint64_val.extend([x.item() for x in proto_values])
def SlowAppendComplex64ArrayToTensorProto(tensor_proto, proto_values): def SlowAppendComplex64ArrayToTensorProto(tensor_proto, proto_values):
tensor_proto.scomplex_val.extend( tensor_proto.scomplex_val.extend(
[np.asscalar(v) for x in proto_values for v in [x.real, x.imag]]) [v.item() for x in proto_values for v in [x.real, x.imag]])
def SlowAppendComplex128ArrayToTensorProto(tensor_proto, proto_values): def SlowAppendComplex128ArrayToTensorProto(tensor_proto, proto_values):
tensor_proto.dcomplex_val.extend( tensor_proto.dcomplex_val.extend(
[np.asscalar(v) for x in proto_values for v in [x.real, x.imag]]) [v.item() for x in proto_values for v in [x.real, x.imag]])
def SlowAppendObjectArrayToTensorProto(tensor_proto, proto_values): def SlowAppendObjectArrayToTensorProto(tensor_proto, proto_values):
tensor_proto.string_val.extend([compat.as_bytes(x) for x in proto_values]) tensor_proto.string_val.extend([compat.as_bytes(x) for x in proto_values])
def SlowAppendBoolArrayToTensorProto(tensor_proto, proto_values): def SlowAppendBoolArrayToTensorProto(tensor_proto, proto_values):
tensor_proto.bool_val.extend([np.asscalar(x) for x in proto_values]) tensor_proto.bool_val.extend([x.item() for x in proto_values])
_NP_TO_APPEND_FN = { _NP_TO_APPEND_FN = {
dtypes.bfloat16.as_numpy_dtype: SlowAppendBFloat16ArrayToTensorProto, dtypes.bfloat16.as_numpy_dtype: SlowAppendBFloat16ArrayToTensorProto,

2
tensorflow/python/kernel_tests/cwise_ops_test.py
View File

@ -595,7 +595,7 @@ class MinMaxOpTest(test.TestCase):
def testScalar(self): def testScalar(self):
x = np.random.rand(1, 3, 2) * 100. x = np.random.rand(1, 3, 2) * 100.
y = np.asscalar(np.random.rand(1) * 100.) # should broadcast y = np.random.rand(1).item() * 100. # should broadcast
# dropped np.float64, int64 because TF automatically converts to 32 bit # dropped np.float64, int64 because TF automatically converts to 32 bit
for t in [np.float32, np.int32]: for t in [np.float32, np.int32]:
self._compare(x.astype(t), t(y), use_gpu=False) self._compare(x.astype(t), t(y), use_gpu=False)

2
tensorflow/python/ops/nn_ops.py
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@ -2334,7 +2334,7 @@ def leaky_relu(features, alpha=0.2, name=None):
features = math_ops.to_float(features) features = math_ops.to_float(features)
if compat.forward_compatible(2018, 11, 1): if compat.forward_compatible(2018, 11, 1):
if isinstance(alpha, np.ndarray): if isinstance(alpha, np.ndarray):
alpha = np.asscalar(alpha) alpha = alpha.item()
return gen_nn_ops.leaky_relu(features, alpha=alpha, name=name) return gen_nn_ops.leaky_relu(features, alpha=alpha, name=name)
alpha = ops.convert_to_tensor(alpha, dtype=features.dtype, name="alpha") alpha = ops.convert_to_tensor(alpha, dtype=features.dtype, name="alpha")
return math_ops.maximum(alpha * features, features, name=name) return math_ops.maximum(alpha * features, features, name=name)