This is an automated email from the ASF dual-hosted git repository.
reminisce pushed a commit to branch numpy
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
commit 70ef8818cbd6c79f508eaeaae36d64ba2f5fe601
Author: reminisce <wujun....@gmail.com>
AuthorDate: Sun May 26 22:41:28 2019 -0700
Change np_compat to np_shape
---
python/mxnet/gluon/block.py | 2 +-
python/mxnet/gluon/parameter.py | 10 +++++-----
python/mxnet/gluon/utils.py | 4 ++--
python/mxnet/ndarray/numpy/_op.py | 3 +--
python/mxnet/ndarray/register.py | 4 ++--
python/mxnet/numpy/__init__.py | 2 +-
python/mxnet/numpy/multiarray.py | 8 +++-----
tests/python/unittest/test_numpy_gluon.py | 6 +++---
tests/python/unittest/test_numpy_ndarray.py | 20 ++++++++++----------
tests/python/unittest/test_numpy_op.py | 16 ++++++++--------
10 files changed, 36 insertions(+), 39 deletions(-)
diff --git a/python/mxnet/gluon/block.py b/python/mxnet/gluon/block.py
index e427d0d..9a1d16e 100644
--- a/python/mxnet/gluon/block.py
+++ b/python/mxnet/gluon/block.py
@@ -543,7 +543,7 @@ class Block(object):
for hook in self._forward_hooks.values():
hook(self, args, out)
- if _mx_np.is_np_compat():
+ if _mx_np.is_np_shape():
_check_all_np_ndarrays(_flatten(out, "output")[0])
return out
diff --git a/python/mxnet/gluon/parameter.py b/python/mxnet/gluon/parameter.py
index 59446c2..0b87bf0 100644
--- a/python/mxnet/gluon/parameter.py
+++ b/python/mxnet/gluon/parameter.py
@@ -31,7 +31,7 @@ from .. import symbol, ndarray, initializer, context
from ..context import Context, cpu
from .. import autograd
from .utils import _indent, _brief_print_list, shape_is_known
-from ..util import is_np_compat
+from ..util import is_np_shape
# pylint: disable= invalid-name
tensor_types = (symbol.Symbol, ndarray.NDArray)
@@ -163,7 +163,7 @@ class Parameter(object):
if self._shape is None:
self._shape = new_shape
return
- unknown_dim_size = -1 if is_np_compat() else 0
+ unknown_dim_size = -1 if is_np_shape() else 0
assert len(self._shape) == len(new_shape) and \
all(j in (unknown_dim_size, i) for i, j in zip(new_shape,
self._shape)), \
"Expected shape %s is incompatible with given shape %s."%(
@@ -282,7 +282,7 @@ class Parameter(object):
initializer.create(default_init)(
initializer.InitDesc(self.name, {'__init__': init}), data)
# TODO(junwu): use np random operators when available
- if is_np_compat():
+ if is_np_shape():
data = data.as_np_ndarray() # convert to np.ndarray
self._init_impl(data, ctx)
@@ -309,7 +309,7 @@ class Parameter(object):
self._grad = [ndarray.zeros(shape=i.shape, dtype=i.dtype,
ctx=i.context,
stype=self._grad_stype) for i in
self._data]
# TODO(junwu): use np.zeros
- if is_np_compat():
+ if is_np_shape():
self._grad = [arr.as_np_ndarray() for arr in self._grad]
autograd.mark_variables(self._check_and_get(self._data, list),
@@ -558,7 +558,7 @@ class Parameter(object):
self._var = symbol.var(self.name, shape=self.shape,
dtype=self.dtype,
lr_mult=self.lr_mult, wd_mult=self.wd_mult,
init=self.init, stype=self._stype)
- if is_np_compat():
+ if is_np_shape():
self._var = self._var.as_np_ndarray()
return self._var
diff --git a/python/mxnet/gluon/utils.py b/python/mxnet/gluon/utils.py
index 418cf41..e8ffdd6 100644
--- a/python/mxnet/gluon/utils.py
+++ b/python/mxnet/gluon/utils.py
@@ -38,7 +38,7 @@ except ImportError:
import numpy as np
from .. import ndarray
-from ..util import is_np_compat
+from ..util import is_np_shape
def split_data(data, num_slice, batch_axis=0, even_split=True):
@@ -453,7 +453,7 @@ def shape_is_known(shape):
"""Check whether a shape is completely known w/ or w/o np semantics."""
if shape is None:
return False
- unknown_dim_size = -1 if is_np_compat() else 0
+ unknown_dim_size = -1 if is_np_shape() else 0
if len(shape) == 0:
return unknown_dim_size == -1
for dim_size in shape:
diff --git a/python/mxnet/ndarray/numpy/_op.py
b/python/mxnet/ndarray/numpy/_op.py
index 725fba4..72b890d 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -20,7 +20,7 @@
from __future__ import absolute_import
import numpy as _np
from ...base import numeric_types
-from ...util import _sanity_check_params, use_np_compat, set_module
+from ...util import _sanity_check_params, set_module
from ...context import current_context
from . import _internal as _npi
@@ -90,7 +90,6 @@ def ones(shape, dtype=None, **kwargs):
#pylint: disable= too-many-arguments, no-member, protected-access
-@use_np_compat
def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None,
out=None):
""" Helper function for element-wise operation.
The function will perform numpy-like broadcasting if needed and call
different functions.
diff --git a/python/mxnet/ndarray/register.py b/python/mxnet/ndarray/register.py
index e93a74c..c2225bb 100644
--- a/python/mxnet/ndarray/register.py
+++ b/python/mxnet/ndarray/register.py
@@ -25,7 +25,7 @@ from ._internal import NDArrayBase, _imperative_invoke #
pylint: disable=unused-
from ..ndarray_doc import _build_doc
from ..base import mx_uint, check_call, _LIB, py_str, _init_op_module, _Null,
_is_np_op # pylint: disable=unused-import
-from ..util import use_np_compat # pylint: disable=unused-import
+from ..util import use_np_shape # pylint: disable=unused-import
def _verify_all_np_ndarrays(op_name, func_name, args, out):
@@ -176,7 +176,7 @@ def _generate_ndarray_function_code(handle, op_name,
func_name, signature_only=F
if is_np_op:
doc_str_idx = 2
code.append("""
-@use_np_compat""")
+@use_np_shape""")
if arr_name:
code.append("""
def %s(*%s, **kwargs):"""%(func_name, arr_name))
diff --git a/python/mxnet/numpy/__init__.py b/python/mxnet/numpy/__init__.py
index 6d6ac6a..6f1c02d 100644
--- a/python/mxnet/numpy/__init__.py
+++ b/python/mxnet/numpy/__init__.py
@@ -26,6 +26,6 @@ from .multiarray import * # pylint: disable=wildcard-import
from . import _op
from . import _register
from ._op import * # pylint: disable=wildcard-import
-from ..util import use_np_compat, set_np_compat, np_compat, is_np_compat
+from ..util import use_np_shape, set_np_shape, np_shape, is_np_shape
__all__ = []
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index f5a3b83..e9afd23 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -30,7 +30,7 @@ from ..ndarray._internal import _set_np_ndarray_class
from . import _op as _mx_np_op
from ..base import check_call, _LIB, NDArrayHandle
from ..base import mx_real_t, c_array_buf, mx_uint, numeric_types
-from ..util import _sanity_check_params, set_module, use_np_compat
+from ..util import _sanity_check_params, set_module, use_np_shape
from ..context import current_context
from ..ndarray import numpy as _mx_nd_np
from ..ndarray.numpy import _internal as _npi
@@ -75,7 +75,7 @@ _set_np_ndarray_class(_np_ndarray_cls)
@set_module('mxnet.numpy') # pylint: disable=invalid-name
-@use_np_compat
+@use_np_shape
class ndarray(NDArray):
"""An array object represents a multidimensional, homogeneous array of
fixed-size items.
An associated data-type object describes the format of each element in the
array
@@ -1140,7 +1140,6 @@ class ndarray(NDArray):
return len(self.shape)
@property
- @use_np_compat
def size(self):
"""Number of elements in the array."""
return super(ndarray, self).size
@@ -1150,7 +1149,6 @@ class ndarray(NDArray):
@set_module('mxnet.numpy')
-@use_np_compat
def empty(shape, dtype=None, **kwargs):
"""Return a new array of given shape and type, without initializing
entries.
@@ -1183,7 +1181,7 @@ def empty(shape, dtype=None, **kwargs):
@set_module('mxnet.numpy')
-@use_np_compat
+@use_np_shape
def array(object, dtype=None, **kwargs):
"""
Create an array.
diff --git a/tests/python/unittest/test_numpy_gluon.py
b/tests/python/unittest/test_numpy_gluon.py
index 446f5b8..b7656b7 100644
--- a/tests/python/unittest/test_numpy_gluon.py
+++ b/tests/python/unittest/test_numpy_gluon.py
@@ -44,7 +44,7 @@ def test_create_np_param():
def hybrid_forward(self, F, x, w):
return F.dot(x, w)
- @np.use_np_compat
+ @np.use_np_shape
class TestBlock2(gluon.HybridBlock):
def __init__(self):
super(TestBlock2, self).__init__()
@@ -62,7 +62,7 @@ def test_create_np_param():
def test_optimizer_with_np_ndarrays():
- @np.use_np_compat
+ @np.use_np_shape
class LinearRegression(gluon.HybridBlock):
def __init__(self, num_input_dim=-1, num_hidden_dim=100,
num_output_dim=10):
super(LinearRegression, self).__init__()
@@ -78,7 +78,7 @@ def test_optimizer_with_np_ndarrays():
y_pred = h_relu.dot(w2) # equivalent to F.np.dot(h_relu, w2)
return y_pred
- @np.use_np_compat
+ @np.use_np_shape
class TotalLoss(gluon.HybridBlock):
def hybrid_forward(self, F, pred, label):
return ((pred - label) ** 2).sum() # equivalent to
F.np.sum(F.np.square(pred - label))
diff --git a/tests/python/unittest/test_numpy_ndarray.py
b/tests/python/unittest/test_numpy_ndarray.py
index 7ffa774..188cb6f 100644
--- a/tests/python/unittest/test_numpy_ndarray.py
+++ b/tests/python/unittest/test_numpy_ndarray.py
@@ -47,7 +47,7 @@ def test_array_creation():
@with_seed()
def test_zeros():
# test np.zeros in Gluon
- @np.use_np_compat
+ @np.use_np_shape
class TestZeros(HybridBlock):
def __init__(self, shape, dtype=None):
super(TestZeros, self).__init__()
@@ -57,13 +57,13 @@ def test_zeros():
def hybrid_forward(self, F, x, *args, **kwargs):
return x + F.np.zeros(shape, dtype)
- @np.use_np_compat
+ @np.use_np_shape
class TestZerosOutputType(HybridBlock):
def hybrid_forward(self, F, x, *args, **kwargs):
return x, F.np.zeros(shape=())
# test np.zeros in imperative
- @np.use_np_compat
+ @np.use_np_shape
def check_zero_array_creation(shape, dtype):
np_out = _np.zeros(shape=shape, dtype=dtype)
mx_out = np.zeros(shape=shape, dtype=dtype)
@@ -97,7 +97,7 @@ def test_zeros():
@with_seed()
def test_ones():
# test np.ones in Gluon
- @np.use_np_compat
+ @np.use_np_shape
class TestOnes(HybridBlock):
def __init__(self, shape, dtype=None):
super(TestOnes, self).__init__()
@@ -107,13 +107,13 @@ def test_ones():
def hybrid_forward(self, F, x, *args, **kwargs):
return x * F.np.ones(shape, dtype)
- @np.use_np_compat
+ @np.use_np_shape
class TestOnesOutputType(HybridBlock):
def hybrid_forward(self, F, x, *args, **kwargs):
return x, F.np.ones(shape=())
# test np.ones in imperative
- @np.use_np_compat
+ @np.use_np_shape
def check_ones_array_creation(shape, dtype):
np_out = _np.ones(shape=shape, dtype=dtype)
mx_out = np.ones(shape=shape, dtype=dtype)
@@ -156,7 +156,7 @@ def test_ndarray_binary_element_wise_ops():
def get_np_ret(x1, x2, op):
return np_op_map[op](x1, x2)
- @np.use_np_compat
+ @np.use_np_shape
class TestBinaryElementWiseOp(HybridBlock):
def __init__(self, op, scalar=None, reverse=False):
super(TestBinaryElementWiseOp, self).__init__()
@@ -219,7 +219,7 @@ def test_ndarray_binary_element_wise_ops():
print(self._op)
assert False
- @np.use_np_compat
+ @np.use_np_shape
def check_binary_op_result(shape1, shape2, op, dtype=None):
if shape1 is None:
mx_input1 = abs(_np.random.uniform()) + 1
@@ -289,7 +289,7 @@ def test_ndarray_binary_element_wise_ops():
@with_seed()
def test_hybrid_block_multiple_outputs():
- @np.use_np_compat
+ @np.use_np_shape
class TestAllNumpyOutputs(HybridBlock):
def hybrid_forward(self, F, x, *args, **kwargs):
return F.npe.relu(x), F.np.sum(x)
@@ -309,7 +309,7 @@ def test_hybrid_block_multiple_outputs():
assert type(out1) is expected_out_type
assert type(out2) is expected_out_type
- @np.use_np_compat
+ @np.use_np_shape
class TestMixedTypeOutputsFailure(HybridBlock):
def hybrid_forward(self, F, x, *args, **kwargs):
return F.relu(x.as_classic_ndarray()), F.np.sum(x)
diff --git a/tests/python/unittest/test_numpy_op.py
b/tests/python/unittest/test_numpy_op.py
index e199392..e43b91f 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -88,7 +88,7 @@ def test_np_sum():
@with_seed()
-@np.use_np_compat
+@np.use_np_shape
def test_np_dot():
shapes = [
((3, 0), (0, 4)),
@@ -132,7 +132,7 @@ def test_np_dot():
@with_seed()
def test_np_mean():
- @np.use_np_compat
+ @np.use_np_shape
class TestMean(HybridBlock):
def __init__(self, axis=None, dtype=None, keepdims=False):
super(TestMean, self).__init__()
@@ -194,7 +194,7 @@ def test_np_mean():
@with_seed()
-@np.use_np_compat
+@np.use_np_shape
def test_np_transpose():
# TODO(junwu): Add more test cases
data = mx.sym.var('a').as_np_ndarray()
@@ -224,7 +224,7 @@ def test_np_transpose():
@with_seed()
-@np.use_np_compat
+@np.use_np_shape
def test_relu():
# TODO(junwu): Add more test cases
data = mx.sym.var('data').as_np_ndarray()
@@ -240,7 +240,7 @@ def test_relu():
@with_seed()
-@np.use_np_compat
+@np.use_np_shape
def test_sigmoid():
# TODO(junwu): Add more test cases
data = mx.sym.var('data').as_np_ndarray()
@@ -256,7 +256,7 @@ def test_sigmoid():
@with_seed()
-@np.use_np_compat
+@np.use_np_shape
def test_np_reshape():
# TODO(junwu): Add more test cases
data = mx.sym.var('a').as_np_ndarray()
@@ -272,7 +272,7 @@ def test_np_reshape():
@with_seed()
-@np.use_np_compat
+@np.use_np_shape
def test_np_maximum():
# TODO(junwu): Add more test cases
x1, x2 = mx.sym.var('x1').as_np_ndarray(), mx.sym.var('x2').as_np_ndarray()
@@ -293,7 +293,7 @@ def test_np_maximum():
@with_seed()
-@np.use_np_compat
+@np.use_np_shape
def test_np_minimum():
# TODO(junwu): Add more test cases
x1, x2 = mx.sym.var('x1').as_np_ndarray(), mx.sym.var('x2').as_np_ndarray()
