未验证 提交 f88a8ba9 编写于 作者: Y Yibing Liu 提交者: GitHub

Merge pull request #12793 from kuke/wrap_squeezes

Wrap unsqueeze & squeeze ops
......@@ -145,6 +145,8 @@ paddle.fluid.layers.smooth_l1 ArgSpec(args=['x', 'y', 'inside_weight', 'outside_
paddle.fluid.layers.one_hot ArgSpec(args=['input', 'depth'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.autoincreased_step_counter ArgSpec(args=['counter_name', 'begin', 'step'], varargs=None, keywords=None, defaults=(None, 1, 1))
paddle.fluid.layers.reshape ArgSpec(args=['x', 'shape', 'actual_shape', 'act', 'inplace', 'name'], varargs=None, keywords=None, defaults=(None, None, True, None))
paddle.fluid.layers.squeeze ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.unsqueeze ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.lod_reset ArgSpec(args=['x', 'y', 'target_lod'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.lrn ArgSpec(args=['input', 'n', 'k', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(5, 1.0, 0.0001, 0.75, None))
paddle.fluid.layers.pad ArgSpec(args=['x', 'paddings', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None))
......
......@@ -85,6 +85,8 @@ __all__ = [
'one_hot',
'autoincreased_step_counter',
'reshape',
'squeeze',
'unsqueeze',
'lod_reset',
'lrn',
'pad',
......@@ -4532,6 +4534,89 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
return helper.append_activation(out)
def squeeze(input, axes, name=None):
"""
Remove single-dimensional entries from the shape of a tensor. Takes a
parameter axes with a list of axes to squeeze. If axes is not provided, all
the single dimensions will be removed from the shape. If an axis is
selected with shape entry not equal to one, an error is raised.
Examples:
Case 1:
Given
X.shape = (1, 3, 1, 5)
and
axes = [0]
we get:
Out.shape = (3, 1, 5)
Case 2:
Given
X.shape = (1, 3, 1, 5)
and
axes = []
we get:
Out.shape = (3, 5)
Args:
input (Variable): The input variable to be squeezed.
axes (list): List of integers, indicating the dimensions to be squeezed.
name (str|None): Name for this layer.
Returns:
Variable: Output squeezed variable.
Examples:
.. code-block:: python
x = layers.data(name='x', shape=[5, 1, 10])
y = layers.sequeeze(input=x, axes=[1])
"""
helper = LayerHelper("squeeze", **locals())
out = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op(
type="squeeze",
inputs={"X": input},
attrs={"axes": axes},
outputs={"Out": out})
return out
def unsqueeze(input, axes, name=None):
"""
Insert single-dimensional entries to the shape of a tensor. Takes one
required argument axes, a list of dimensions that will be inserted.
Dimension indices in axes are as seen in the output tensor.
For example:
Given a tensor such that tensor with shape [3, 4, 5],
then Unsqueezed tensor with axes=[0, 4] has shape [1, 3, 4, 5, 1].
Args:
input (Variable): The input variable to be unsqueezed.
axes (list): List of integers, indicating the dimensions to be inserted.
name (str|None): Name for this layer.
Returns:
Variable: Output unsqueezed variable.
Examples:
.. code-block:: python
x = layers.data(name='x', shape=[5, 10])
y = layers.unsequeeze(input=x, axes=[1])
"""
helper = LayerHelper("unsqueeze", **locals())
out = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op(
type="unsqueeze",
inputs={"X": input},
attrs={"axes": axes},
outputs={"Out": out})
return out
def lod_reset(x, y=None, target_lod=None):
"""
Set LoD of :attr:`x` to a new one specified by :attr:`y` or
......
......@@ -240,6 +240,22 @@ class TestBook(unittest.TestCase):
self.assertIsNotNone(layers.softmax(hid))
print(str(program))
def test_sequence_unsqueeze(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[8, 2], dtype='float32')
out = layers.unsqueeze(input=x, axes=[1])
self.assertIsNotNone(out)
print(str(program))
def test_squeeze(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[1, 1, 4], dtype='float32')
out = layers.squeeze(input=x, axes=[2])
self.assertIsNotNone(out)
print(str(program))
def test_lrn(self):
program = Program()
with program_guard(program):
......
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