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未验证 提交 d3c9db75 编写于 作者: Z zhongpu 提交者： GitHub
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copy api from paddle to paddle.fluid (#24164) 

* copy api from paddle to paddle.fluid, test=develop

* fix optest, test=develop
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Showing with 1827 addition and 315 deletion
python/paddle/fluid/dygraph/nn.py
浏览文件 @ d3c9db75
此差异已折叠。
python/paddle/fluid/layers/nn.py
浏览文件 @ d3c9db75
此差异已折叠。
python/paddle/fluid/layers/tensor.py
浏览文件 @ d3c9db75
......@@ -18,8 +18,8 @@ from six.moves import reduce
from ..layer_helper import LayerHelper
from ..param_attr import ParamAttr
from ..initializer import Initializer
from ..framework import convert_np_dtype_to_dtype_, in_dygraph_mode, _varbase_creator
from ..framework import Variable
from ..framework import convert_np_dtype_to_dtype_, in_dygraph_mode, _varbase_creator, device_guard, OpProtoHolder
from ..framework import Variable, in_dygraph_mode
from ..initializer import Constant
from ..core import VarDesc
from .. import core
......@@ -30,32 +30,12 @@ import numpy
import warnings
__all__ = [
'create_tensor',
'create_parameter',
'create_global_var',
'cast',
'tensor_array_to_tensor',
'concat',
'sums',
'assign',
'fill_constant_batch_size_like',
'fill_constant',
'argmin',
'argmax',
'argsort',
'ones',
'zeros',
'reverse',
'has_inf',
'has_nan',
'isfinite',
'range',
'linspace',
'zeros_like',
'ones_like',
'diag',
'eye',
'kron',
'create_tensor', 'create_parameter', 'create_global_var', 'cast',
'tensor_array_to_tensor', 'concat', 'sums', 'assign',
'fill_constant_batch_size_like', 'fill_constant', 'argmin', 'argmax',
'argsort', 'ones', 'zeros', 'reverse', 'has_inf', 'has_nan', 'isfinite',
'range', 'linspace', 'zeros_like', 'ones_like', 'diag', 'eye', 'kron',
'full_like', 'arange', 'full', 'tril', 'triu'
]
......@@ -1587,6 +1567,412 @@ def ones_like(x, out=None):
return out
def full_like(input,
fill_value,
out=None,
dtype=None,
device=None,
stop_gradient=True,
name=None):
"""
**full_like**
This function creates a tensor filled with `fill_value` which has identical shape and dtype
with `input`.
Args:
input(Variable): The input tensor which specifies shape and dtype.
fill_value: The value to fill the tensor with. Data type can be bool, float32, float64, int32, int64. Default value is 0.
out(Variable): The output tensor.
Returns:
out(Variable): The tensor variable storing the output.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
input = fluid.data(name='input', dtype='float32', shape=[2, 3])
output = fluid.layers.full_like(input, 2.0)
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())
img=np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
res = exe.run(fluid.default_main_program(), feed={'input':img}, fetch_list=[output])
print(res) # [array([[2., 2., 2.], [2., 2., 2.]], dtype=float32)]
"""
helper = LayerHelper("full_like", **locals())
if dtype is None:
dtype = 'float32'
check_dtype(dtype, 'dtype',
['bool', 'float16', 'float32', 'int32', 'int64'], 'full_like')
if out is None:
out = helper.create_variable_for_type_inference(dtype=dtype)
helper.append_op(
type='fill_any_like',
inputs={'X': [input]},
attrs={'value': fill_value},
outputs={'Out': [out]})
out.stop_gradient = stop_gradient
return out
def arange(start, end, step=1, dtype=None, name=None):
"""
Return evenly spaced values within a given interval.
Values are generated within the half-open interval [start, stop) (in other words,
the interval including start but excluding stop).
Parameters:
start(float32 | float64 | int32 | int64 | Variable): Start of interval. The interval includes this value.
when start is Variable, it is a 1-D Tensor with shape [1].
end(float32 | float64 | int32 | int64 | Variable): End of interval. The interval does not include this
value, except in some cases where step is not an integer
and floating point round-off affects the length of out. When end is Variable,
it is a 1-D Tensor with shape [1].
step(float32 | float64 | int32 | int64 | Variable): Spacing between values. For any output out, this is the
distance between two adjacent values, out[i+1] - out[i].
dtype(str|core.VarDesc.VarType): the data type of the output tensor, can be float32, float64, int32, int64.
Returns: a 1-D Tensor which is evenly spaced values within a given interval. Its data type is set by dtype.
Return type: Variable
examples:
.. code-block:: python
import paddle.fluid as fluid
# expected out put: [0, 2, 4, 6, 8]
data = fluid.layers.arange(0, 10, 2, 'int32')
#dygraph mode
import paddle.fluid as fluid
with fluid.dygraph.guard():
x = fluid.layers.arange(0, 6, 2)
# x: [0, 2, 4]
# x dtype: float32
"""
helper = LayerHelper("range", **locals())
if dtype is None:
dtype = 'float32'
check_dtype(dtype, 'create data type',
['float32', 'float64', 'int32', 'int64'], 'range')
dtype = convert_dtype(dtype)
if not isinstance(start, Variable):
start = fill_constant([1], dtype, start)
if not isinstance(end, Variable):
end = fill_constant([1], dtype, end)
if not isinstance(step, Variable):
step = fill_constant([1], dtype, step)
out = helper.create_variable_for_type_inference(dtype=start.dtype)
helper.append_op(
type='range',
inputs={'Start': start,
'End': end,
'Step': step},
outputs={'Out': [out]})
out.stop_gradient = True
return out
def full(shape,
fill_value,
out=None,
dtype=None,
device=None,
stop_gradient=True,
name=None):
"""
This Op return a Tensor with the `fill_value` which size is same as `shape`
Args:
shape(list|tuple|Variable): Shape of the Tensor to be created.
The data type is ``int32`` or ``int64`` . If ``shape`` is a list or tuple,
the elements of it should be integers or Tensors with shape [1].
If ``shape`` is an Variable, it should be an 1-D Tensor .
fill_value(bool|float16|float32|float64|int32|int64|Variable): The constant value
used to initialize the Tensor to be created. If fill_value is an Variable, it must be an 1-D Tensor.
out(Variable, optional): Optional output which can be any created
Variable that meets the requirements to store the result of operation.
if out is None, a new Varibale will be create to store the result.
dtype(np.dtype|core.VarDesc.VarType|str, optional): Data type of the output tensor
which can be float16, float32, float64, int32, int64, if dytpe is `None`, the data
type of created tensor is `float32`
device(str, optional): On which device to run this Op. The :attr:`device` must be
None, 'cpu' or 'gpu'. If :attr:`device` is None, the device that the user set in
the paddle program will be chosen. Default value is None.
stop_gradient(bool, optional): Indicating if we stop gradient from current(out) Variable,
default value is True.
name(str, optional): The default value is None. Normally there is no need for user to set this
property. For more information, please refer to :ref:`api_guide_Name`.
Returns:
Variable: Tensor which is created according to shape and dtype.
Raises:
TypeError: The `dtype` must be one of None, bool, float16, float32, float64, int32 and int64.
TypeError: The `out` must be a Variable.
TypeError: The `shape` must be one of Variable, list tuple.
Examples:
.. code-block:: python
import paddle.fluid as fluid
data1 = fluid.layers.full(shape=[2,1], fill_value=0, dtype='int64') # data1=[[0],[0]]
data2 = fluid.layers.full(shape=[2,1], fill_value=5, dtype='int64', device='gpu') # data2=[[5],[5]]
# attr shape is a list which contains Variable Tensor.
positive_2 = fluid.layers.fill_constant([1], "int32", 2)
data3 = fluid.layers.full(shape=[1, positive_2], dtype='float32', fill_value=1.5) # data3=[1.5, 1.5]
# attr shape is an Variable Tensor.
shape = fluid.layers.fill_constant([1,2], "int32", 2) # shape=[2,2]
data4 = fluid.layers.full(shape=shape, dtype='bool', fill_value=True) # data4=[[True,True],[True,True]]
# attr value is an Variable Tensor.
val = fluid.layers.fill_constant([1], "float32", 2.0) # val=[2.0]
data5 = fluid.layers.full(shape=[2,1], fill_value=val, dtype='float32') #data5=[[2.0],[2.0]]
"""
helper = LayerHelper("full", **locals())
if dtype is None:
dtype = 'float32'
check_dtype(dtype, 'create data type',
['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
'full')
check_type(shape, 'shape', (Variable, list, tuple), 'full')
if out is not None:
check_type(shape, 'out', (Variable), 'full')
if out is None:
out = helper.create_variable_for_type_inference(dtype=dtype)
out.stop_gradient = stop_gradient
with device_guard(device):
out = fill_constant(shape=shape, dtype=dtype, value=fill_value, out=out)
return out
def _tril_triu_op(helper):
"""Base op of tril_op and triu_op
"""
op_type = helper.layer_type
x = helper.kwargs.get('input', None)
assert x is not None, 'x cannot be None in {}'.format(op_type)
check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],
op_type)
if len(x.shape) < 2:
raise ValueError("input shape in {} must be at least 2-D".format(
op_type))
diagonal = helper.kwargs.get('diagonal', 0)
if not isinstance(diagonal, (int, )):
raise TypeError("diagonal in {} must be a python Int".format(op_type))
name = helper.kwargs.get('name', None)
if name is None:
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
helper.append_op(
type="tril_triu",
inputs={"X": x},
attrs={
"diagonal": diagonal,
"lower": True if op_type == 'tril' else False,
},
outputs={"Out": out}, )
return out
def tril(input, diagonal=0, name=None):
"""
This op returns the lower triangular part of a matrix (2-D tensor) or batch
of matrices :attr:`input`, the other elements of the result tensor are set
to 0. The lower triangular part of the matrix is defined as the elements
on and below the diagonal.
Args:
input (Variable): The input variable which is a Tensor.
Support data types: ``float64``, ``float32``, ``int32``, ``int64``.
diagonal (int, optional): The diagonal to consider, default value is 0.
If :attr:`diagonal` = 0, all elements on and below the main diagonal are
retained. A positive value includes just as many diagonals above the main
diagonal, and similarly a negative value excludes just as many diagonals below
the main diagonal. The main diagonal are the set of indices
:math:`\{(i, i)\}` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]` where
:math:`d_{1}, d_{2}` are the dimensions of the matrix.
name (str, optional): The default value is None. Normally there is no need for
user to set this property. For more information, please refer to :ref:`api_guide_Name`.
Returns:
Variable: Tensor, results of lower triangular operation by the specified diagonal of input tensor,
it's data type is the same as input's Tensor.
Raises:
TypeError: diagonal is not a int type.
ValueError: dimension of :attr:`input` is less than 2.
Examples:
.. code-block:: python
import numpy as np
import paddle.fluid as fluid
data = np.arange(1, 13, dtype="int64").reshape(3,-1)
# array([[ 1, 2, 3, 4],
# [ 5, 6, 7, 8],
# [ 9, 10, 11, 12]])
x = fluid.data(shape=(-1, 4), dtype='int64', name='x')
exe = fluid.Executor(fluid.CPUPlace())
# example 1, default diagonal
tril = fluid.layers.tril(x)
tril_out, = exe.run(fluid.default_main_program(), feed={"x": data},
fetch_list=[tril], return_numpy=True)
# array([[ 1, 0, 0, 0],
# [ 5, 6, 0, 0],
# [ 9, 10, 11, 0]])
.. code-block:: python
# example 2, positive diagonal value
import paddle.fluid as fluid
import numpy as np
data = np.arange(1, 13, dtype="int64").reshape(3,-1)
x = fluid.data(shape=(-1, 4), dtype='int64', name='x')
exe = fluid.Executor(fluid.CPUPlace())
tril = fluid.layers.tril(x, diagonal=2)
tril_out, = exe.run(fluid.default_main_program(), feed={"x": data},
fetch_list=[tril], return_numpy=True)
# array([[ 1, 2, 3, 0],
# [ 5, 6, 7, 8],
# [ 9, 10, 11, 12]])
.. code-block:: python
# example 3, negative diagonal value
import paddle.fluid as fluid
import numpy as np
data = np.arange(1, 13, dtype="int64").reshape(3,-1)
x = fluid.data(shape=(-1, 4), dtype='int64', name='x')
exe = fluid.Executor(fluid.CPUPlace())
tril = fluid.layers.tril(x, diagonal=-1)
tril_out, = exe.run(fluid.default_main_program(), feed={"x": data},
fetch_list=[tril], return_numpy=True)
# array([[ 0, 0, 0, 0],
# [ 5, 0, 0, 0],
# [ 9, 10, 0, 0]])
"""
return _tril_triu_op(LayerHelper('tril', **locals()))
def triu(input, diagonal=0, name=None):
"""
This op returns the upper triangular part of a matrix (2-D tensor) or batch of matrices
:attr:`input`, the other elements of the result tensor are set to 0.
The upper triangular part of the matrix is defined as the elements on and
above the diagonal.
Args:
input (Variable): The input variable which is a Tensor.
Support data types: ``float64``, ``float32``, ``int32``, ``int64``.
diagonal (int, optional): The diagonal to consider, default value is 0.
If :attr:`diagonal` = 0, all elements on and above the main diagonal are
retained. A positive value excludes just as many diagonals above the main
diagonal, and similarly a negative value includes just as many diagonals below
the main diagonal. The main diagonal are the set of indices
:math:`\{(i, i)\}` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]` where
:math:`d_{1}, d_{2}` are the dimensions of the matrix.
name (str, optional): The default value is None. Normally there is no need for
user to set this property. For more information, please refer to :ref:`api_guide_Name`.
Returns:
Variable: Tensor, results of upper triangular operation by the specified diagonal of input tensor,
it's data type is the same as input's Tensor.
Raises:
TypeError: diagonal is not a int type.
ValueError: dimension of :attr:`input` is less than 2.
Examples:
.. code-block:: python
import numpy as np
import paddle.fluid as fluid
data = np.arange(1, 13, dtype="int64").reshape(3,-1)
# array([[ 1, 2, 3, 4],
# [ 5, 6, 7, 8],
# [ 9, 10, 11, 12]])
x = fluid.data(shape=(-1, 4), dtype='int64', name='x')
exe = fluid.Executor(fluid.CPUPlace())
# example 1, default diagonal
import paddle.fluid as fluid
triu = fluid.layers.triu(x)
triu_out, = exe.run(fluid.default_main_program(), feed={"x": data},
fetch_list=[triu], return_numpy=True)
# array([[ 1, 2, 3, 4],
# [ 0, 6, 7, 8],
# [ 0, 0, 11, 12]])
.. code-block:: python
# example 2, positive diagonal value
import paddle.fluid as fluid
import numpy as np
data = np.arange(1, 13, dtype="int64").reshape(3,-1)
x = fluid.data(shape=(-1, 4), dtype='int64', name='x')
exe = fluid.Executor(fluid.CPUPlace())
triu = fluid.layers.triu(x, diagonal=2)
triu_out, = exe.run(fluid.default_main_program(), feed={"x": data},
fetch_list=[triu], return_numpy=True)
# array([[0, 0, 3, 4],
# [0, 0, 0, 8],
# [0, 0, 0, 0]])
.. code-block:: python
# example 3, negative diagonal value
import paddle.fluid as fluid
import numpy as np
data = np.arange(1, 13, dtype="int64").reshape(3,-1)
x = fluid.data(shape=(-1, 4), dtype='int64', name='x')
exe = fluid.Executor(fluid.CPUPlace())
triu = fluid.layers.triu(x, diagonal=-1)
triu_out, = exe.run(fluid.default_main_program(), feed={"x": data},
fetch_list=[triu], return_numpy=True)
# array([[ 1, 2, 3, 4],
# [ 5, 6, 7, 8],
# [ 0, 10, 11, 12]])
"""
return _tril_triu_op(LayerHelper('triu', **locals()))
@templatedoc(op_type="kron")
def kron(x, y, out=None, name=None):
"""${comment}
......
python/paddle/fluid/tests/unittests/test_allclose_layer.py
浏览文件 @ d3c9db75
......@@ -23,9 +23,9 @@ class TestAllcloseLayer(unittest.TestCase):
a = fluid.data(name="a", shape=[2], dtype='float32')
b = fluid.data(name="b", shape=[2], dtype='float32')
result = paddle.allclose(
result = fluid.layers.allclose(
a, b, rtol=1e-05, atol=1e-08, equal_nan=False, name="ignore_nan")
result_nan = paddle.allclose(
result_nan = fluid.layers.allclose(
a, b, rtol=1e-05, atol=1e-08, equal_nan=True, name="equal_nan")
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
......@@ -82,7 +82,7 @@ class TestAllcloseLayer(unittest.TestCase):
with fluid.dygraph.guard():
x_v_1 = fluid.dygraph.to_variable(x_1)
y_v_1 = fluid.dygraph.to_variable(y_1)
ret_1 = paddle.allclose(
ret_1 = fluid.layers.allclose(
x_v_1,
y_v_1,
rtol=1e-05,
......@@ -90,7 +90,7 @@ class TestAllcloseLayer(unittest.TestCase):
equal_nan=False,
name='test_1')
self.assertEqual(ret_1.numpy()[0], False)
ret_1 = paddle.allclose(
ret_1 = fluid.layers.allclose(
x_v_1,
y_v_1,
rtol=1e-05,
......@@ -100,7 +100,7 @@ class TestAllcloseLayer(unittest.TestCase):
self.assertEqual(ret_1.numpy()[0], False)
x_v_2 = fluid.dygraph.to_variable(x_2)
y_v_2 = fluid.dygraph.to_variable(y_2)
ret_2 = paddle.allclose(
ret_2 = fluid.layers.allclose(
x_v_2,
y_v_2,
rtol=1e-05,
......@@ -108,7 +108,7 @@ class TestAllcloseLayer(unittest.TestCase):
equal_nan=False,
name='test_3')
self.assertEqual(ret_2.numpy()[0], True)
ret_2 = paddle.allclose(
ret_2 = fluid.layers.allclose(
x_v_2,
y_v_2,
rtol=1e-05,
......@@ -118,7 +118,7 @@ class TestAllcloseLayer(unittest.TestCase):
self.assertEqual(ret_2.numpy()[0], True)
x_v_3 = fluid.dygraph.to_variable(x_3)
y_v_3 = fluid.dygraph.to_variable(y_3)
ret_3 = paddle.allclose(
ret_3 = fluid.layers.allclose(
x_v_3,
y_v_3,
rtol=1e-05,
......@@ -126,7 +126,7 @@ class TestAllcloseLayer(unittest.TestCase):
equal_nan=False,
name='test_5')
self.assertEqual(ret_3.numpy()[0], False)
ret_3 = paddle.allclose(
ret_3 = fluid.layers.allclose(
x_v_3,
y_v_3,
rtol=1e-05,
......
python/paddle/fluid/tests/unittests/test_arange.py
浏览文件 @ d3c9db75
......@@ -14,7 +14,6 @@
from __future__ import print_function
import paddle
import paddle.fluid as fluid
import unittest
import numpy as np
......@@ -71,7 +70,7 @@ class TestInt32ArangeOpCase2(TestArangeOp):
class TestArangeAPI(unittest.TestCase):
def test_out(self):
with fluid.program_guard(fluid.Program()):
data = paddle.arange(0, 5, 1)
data = fluid.layers.arange(0, 5, 1)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
result, = exe.run(fetch_list=[data])
......@@ -79,7 +78,7 @@ class TestArangeAPI(unittest.TestCase):
self.assertEqual((result == expected_data).all(), True)
with fluid.program_guard(fluid.Program()):
data = paddle.arange(0.0, 5.0, 1.0, 'int32')
data = fluid.layers.arange(0.0, 5.0, 1.0, 'int32')
place = fluid.CPUPlace()
exe = fluid.Executor(place)
result, = exe.run(fetch_list=[data])
......
python/paddle/fluid/tests/unittests/test_bce_loss.py
浏览文件 @ d3c9db75
......@@ -36,7 +36,7 @@ class TestBCELoss(unittest.TestCase):
name='input', shape=[None, 30], dtype='float64')
label = fluid.data(
name='label', shape=[None, 30], dtype='float64')
bce_loss = paddle.nn.loss.BCELoss(reduction=red)
bce_loss = fluid.dygraph.BCELoss(reduction=red)
res = bce_loss(input, label)
exe = fluid.Executor(place)
......@@ -47,7 +47,7 @@ class TestBCELoss(unittest.TestCase):
fetch_list=[res])
with fluid.dygraph.guard():
bce_loss = paddle.nn.loss.BCELoss(reduction=red)
bce_loss = fluid.dygraph.BCELoss(reduction=red)
dy_res = bce_loss(
fluid.dygraph.to_variable(input_np),
fluid.dygraph.to_variable(label_np))
......@@ -80,7 +80,7 @@ class TestBCELoss(unittest.TestCase):
name='label', shape=[None, 3, 4, 10], dtype='float64')
weight = fluid.data(
name='weight', shape=[3, 4, 10], dtype='float64')
bce_loss = paddle.nn.loss.BCELoss(weight=weight)
bce_loss = fluid.dygraph.BCELoss(weight=weight)
res = bce_loss(input, label)
exe = fluid.Executor(place)
......@@ -93,7 +93,7 @@ class TestBCELoss(unittest.TestCase):
fetch_list=[res])
with fluid.dygraph.guard():
bce_loss = paddle.nn.loss.BCELoss(
bce_loss = fluid.dygraph.BCELoss(
weight=fluid.dygraph.to_variable(weight_np))
dy_res = bce_loss(
fluid.dygraph.to_variable(input_np),
......
python/paddle/fluid/tests/unittests/test_compare_op.py
浏览文件 @ d3c9db75
......@@ -82,7 +82,7 @@ class API_TestElementwise_Equal(unittest.TestCase):
with fluid.program_guard(fluid.Program(), fluid.Program()):
label = fluid.layers.assign(np.array([3, 3], dtype="int32"))
limit = fluid.layers.assign(np.array([3, 2], dtype="int32"))
out = paddle.elementwise_equal(x=label, y=limit)
out = fluid.layers.elementwise_equal(x=label, y=limit)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
res, = exe.run(fetch_list=[out])
......@@ -91,7 +91,7 @@ class API_TestElementwise_Equal(unittest.TestCase):
with fluid.program_guard(fluid.Program(), fluid.Program()):
label = fluid.layers.assign(np.array([3, 3], dtype="int32"))
limit = fluid.layers.assign(np.array([3, 3], dtype="int32"))
out = paddle.elementwise_equal(x=label, y=limit)
out = fluid.layers.elementwise_equal(x=label, y=limit)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
res, = exe.run(fetch_list=[out])
......
python/paddle/fluid/tests/unittests/test_cross_entropy_loss.py
浏览文件 @ d3c9db75
......@@ -35,7 +35,7 @@ class CrossEntropyLoss(unittest.TestCase):
label = fluid.layers.data(name='label', shape=[5, 1], dtype='int64')
weight = fluid.layers.data(
name='weight', shape=[100], dtype='float32')
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(weight=weight)
cross_entropy_loss = fluid.dygraph.CrossEntropyLoss(weight=weight)
ret = cross_entropy_loss(input, label)
exe = fluid.Executor(place)
......@@ -48,7 +48,7 @@ class CrossEntropyLoss(unittest.TestCase):
fetch_list=[ret])
self.assertIsNotNone(static_ret)
with fluid.dygraph.guard():
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
cross_entropy_loss = fluid.dygraph.CrossEntropyLoss(
weight=fluid.dygraph.to_variable(weight_np))
dy_ret = cross_entropy_loss(
fluid.dygraph.to_variable(input_np),
......@@ -71,7 +71,7 @@ class CrossEntropyLoss(unittest.TestCase):
label = fluid.layers.data(name='label', shape=[5, 1], dtype='int64')
weight = fluid.layers.data(
name='weight', shape=[100], dtype='float32')
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
cross_entropy_loss = fluid.dygraph.CrossEntropyLoss(
weight=weight, reduction='sum')
ret = cross_entropy_loss(input, label)
......@@ -85,7 +85,7 @@ class CrossEntropyLoss(unittest.TestCase):
fetch_list=[ret])
self.assertIsNotNone(static_ret)
with fluid.dygraph.guard():
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
cross_entropy_loss = fluid.dygraph.CrossEntropyLoss(
weight=fluid.dygraph.to_variable(weight_np), reduction='sum')
dy_ret = cross_entropy_loss(
fluid.dygraph.to_variable(input_np),
......@@ -108,7 +108,7 @@ class CrossEntropyLoss(unittest.TestCase):
label = fluid.layers.data(name='label', shape=[5, 1], dtype='int64')
weight = fluid.layers.data(
name='weight', shape=[100], dtype='float32')
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
cross_entropy_loss = fluid.dygraph.CrossEntropyLoss(
weight=weight, reduction='none')
ret = cross_entropy_loss(input, label)
......@@ -122,7 +122,7 @@ class CrossEntropyLoss(unittest.TestCase):
fetch_list=[ret])
self.assertIsNotNone(static_ret)
with fluid.dygraph.guard():
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
cross_entropy_loss = fluid.dygraph.CrossEntropyLoss(
weight=fluid.dygraph.to_variable(weight_np), reduction='none')
dy_ret = cross_entropy_loss(
fluid.dygraph.to_variable(input_np),
......
python/paddle/fluid/tests/unittests/test_fill_any_like_op.py
浏览文件 @ d3c9db75
......@@ -106,7 +106,7 @@ class TestFillAnyLikeOp_attr_out(unittest.TestCase):
with fluid.program_guard(train_program, startup_program):
fill_value = 2.0
input = fluid.data(name='input', dtype='float32', shape=[2, 3])
output = paddle.full_like(input, fill_value)
output = fluid.layers.full_like(input, fill_value)
place = fluid.CPUPlace()
if fluid.core.is_compiled_with_cuda():
......@@ -132,20 +132,20 @@ class TestFillAnyLikeOpError(unittest.TestCase):
#for ci coverage
input_data = fluid.data(name='input', dtype='float32', shape=[2, 3])
output = paddle.full_like(input_data, 2.0)
output = fluid.layers.full_like(input_data, 2.0)
def test_input_dtype():
paddle.full_like
fluid.layers.full_like
self.assertRaises(
ValueError,
paddle.full_like,
fluid.layers.full_like,
input=input_data,
fill_value=2,
dtype='uint4')
self.assertRaises(
TypeError,
paddle.full_like,
fluid.layers.full_like,
input=input_data,
fill_value=2,
dtype='int16')
......
python/paddle/fluid/tests/unittests/test_flip.py
浏览文件 @ d3c9db75
......@@ -16,7 +16,6 @@ from __future__ import print_function
import unittest
import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid import Program, program_guard
......@@ -32,7 +31,7 @@ class TestFlipOp_API(unittest.TestCase):
with fluid.program_guard(train_program, startup_program):
dims = [0]
input = fluid.data(name='input', dtype='float32', shape=[2, 3])
output = paddle.flip(input, dims)
output = fluid.layers.flip(input, dims)
place = fluid.CPUPlace()
if fluid.core.is_compiled_with_cuda():
place = fluid.CUDAPlace(0)
......@@ -52,7 +51,7 @@ class TestFlipOp_API(unittest.TestCase):
img = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
with fluid.dygraph.guard():
inputs = fluid.dygraph.to_variable(img)
ret = paddle.flip(inputs, [0])
ret = fluid.layers.flip(inputs, [0])
out_ref = np.array([[4, 5, 6], [1, 2, 3]]).astype(np.float32)
self.assertTrue(
(ret.numpy() == out_ref).all(),
......
python/paddle/fluid/tests/unittests/test_full_op.py
浏览文件 @ d3c9db75
......@@ -21,7 +21,6 @@ from op_test import OpTest
import paddle.fluid.core as core
from paddle.fluid.op import Operator
import paddle.fluid as fluid
import paddle
from paddle.fluid import compiler, Program, program_guard
......@@ -37,39 +36,39 @@ class TestFullAPI(unittest.TestCase):
shape_tensor_int64 = fluid.data(
name="shape_tensor_int64", shape=[2], dtype="int64")
out_1 = paddle.full(
out_1 = fluid.layers.full(
shape=[1, 2], dtype="float32", fill_value=1.1, device='gpu')
out_2 = paddle.full(
out_2 = fluid.layers.full(
shape=[1, positive_2_int32],
dtype="float32",
fill_value=1.1,
device='cpu')
out_3 = paddle.full(
out_3 = fluid.layers.full(
shape=[1, positive_2_int64],
dtype="float32",
fill_value=1.1,
device='gpu')
out_4 = paddle.full(
out_4 = fluid.layers.full(
shape=shape_tensor_int32,
dtype="float32",
fill_value=1.2,
out=out_3)
out_5 = paddle.full(
out_5 = fluid.layers.full(
shape=shape_tensor_int64,
dtype="float32",
fill_value=1.1,
device='gpu',
stop_gradient=False)
out_6 = paddle.full(
out_6 = fluid.layers.full(
shape=shape_tensor_int64, dtype=np.float32, fill_value=1.1)
val = fluid.layers.fill_constant(shape=[1], dtype=np.float32, value=1.1)
out_7 = paddle.full(
out_7 = fluid.layers.full(
shape=shape_tensor_int64, dtype=np.float32, fill_value=val)
exe = fluid.Executor(place=fluid.CPUPlace())
......@@ -97,17 +96,21 @@ class TestFullOpError(unittest.TestCase):
x1 = fluid.layers.data(name='x1', shape=[1], dtype="int16")
x2 = np.random.randn(1, 2).astype('int32')
self.assertRaises(
ValueError, paddle.full, shape=[1], fill_value=5, dtype='uint4')
ValueError,
fluid.layers.full,
shape=[1],
fill_value=5,
dtype='uint4')
self.assertRaises(
TypeError,
paddle.full,
fluid.layers.full,
shape=[1],
fill_value=5,
dtype='int32',
out=x2)
self.assertRaises(
TypeError,
paddle.full,
fluid.layers.full,
shape=[1],
fill_value=5,
dtype='int16',
......@@ -118,17 +121,21 @@ class TestFullOpError(unittest.TestCase):
x2 = fluid.layers.data(name='x2', shape=[1], dtype="int32")
self.assertRaises(
TypeError, paddle.full, shape=[1], fill_value=5, dtype='uint8')
TypeError,
fluid.layers.full,
shape=[1],
fill_value=5,
dtype='uint8')
# The argument shape's type of full_op must be list, tuple or Variable.
def test_shape_type():
paddle.full(shape=1, dtype="float32", fill_value=1)
fluid.layers.full(shape=1, dtype="float32", fill_value=1)
self.assertRaises(TypeError, test_shape_type)
# The argument shape's size of full_op must not be 0.
def test_shape_size():
paddle.full(shape=[], dtype="float32", fill_value=1)
fluid.layers.full(shape=[], dtype="float32", fill_value=1)
self.assertRaises(AssertionError, test_shape_size)
......@@ -136,14 +143,15 @@ class TestFullOpError(unittest.TestCase):
def test_shape_tensor_dtype():
shape = fluid.data(
name="shape_tensor", shape=[2], dtype="float32")
paddle.full(shape=shape, dtype="float32", fill_value=1)
fluid.layers.full(shape=shape, dtype="float32", fill_value=1)
self.assertRaises(TypeError, test_shape_tensor_dtype)
def test_shape_tensor_list_dtype():
shape = fluid.data(
name="shape_tensor_list", shape=[1], dtype="bool")
paddle.full(shape=[shape, 2], dtype="float32", fill_value=1)
fluid.layers.full(
shape=[shape, 2], dtype="float32", fill_value=1)
self.assertRaises(TypeError, test_shape_tensor_list_dtype)
......
python/paddle/fluid/tests/unittests/test_l1_loss.py
浏览文件 @ d3c9db75
......@@ -33,7 +33,7 @@ class TestL1Loss(unittest.TestCase):
name='input', shape=[10, 1], dtype='float32')
label = fluid.layers.data(
name='label', shape=[10, 1], dtype='float32')
l1_loss = paddle.nn.loss.L1Loss()
l1_loss = fluid.dygraph.L1Loss()
ret = l1_loss(input, label)
exe = fluid.Executor(place)
......@@ -44,7 +44,7 @@ class TestL1Loss(unittest.TestCase):
fetch_list=[ret])
with fluid.dygraph.guard():
l1_loss = paddle.nn.loss.L1Loss()
l1_loss = fluid.dygraph.L1Loss()
dy_ret = l1_loss(
fluid.dygraph.to_variable(input_np),
fluid.dygraph.to_variable(label_np))
......@@ -68,7 +68,7 @@ class TestL1Loss(unittest.TestCase):
name='input', shape=[10, 10, 5], dtype='float32')
label = fluid.layers.data(
name='label', shape=[10, 10, 5], dtype='float32')
l1_loss = paddle.nn.loss.L1Loss(reduction='sum')
l1_loss = fluid.dygraph.L1Loss(reduction='sum')
ret = l1_loss(input, label)
exe = fluid.Executor(place)
......@@ -79,7 +79,7 @@ class TestL1Loss(unittest.TestCase):
fetch_list=[ret])
with fluid.dygraph.guard():
l1_loss = paddle.nn.loss.L1Loss(reduction='sum')
l1_loss = fluid.dygraph.L1Loss(reduction='sum')
dy_ret = l1_loss(
fluid.dygraph.to_variable(input_np),
fluid.dygraph.to_variable(label_np))
......@@ -103,7 +103,7 @@ class TestL1Loss(unittest.TestCase):
name='input', shape=[10, 5], dtype='float32')
label = fluid.layers.data(
name='label', shape=[10, 5], dtype='float32')
l1_loss = paddle.nn.loss.L1Loss(reduction='none')
l1_loss = fluid.dygraph.L1Loss(reduction='none')
ret = l1_loss(input, label)
exe = fluid.Executor(place)
......@@ -114,7 +114,7 @@ class TestL1Loss(unittest.TestCase):
fetch_list=[ret])
with fluid.dygraph.guard():
l1_loss = paddle.nn.loss.L1Loss(reduction='none')
l1_loss = fluid.dygraph.L1Loss(reduction='none')
dy_ret = l1_loss(
fluid.dygraph.to_variable(input_np),
fluid.dygraph.to_variable(label_np))
......
python/paddle/fluid/tests/unittests/test_log_softmax.py
浏览文件 @ d3c9db75
......@@ -17,7 +17,6 @@ import numpy as np
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.nn as nn
import paddle.nn.functional as functional
def stable_softmax(x):
......@@ -84,14 +83,14 @@ class TestNNFunctionalLogSoftmaxAPI(unittest.TestCase):
mylogsoftmax = nn.LogSoftmax(axis)
with fluid.program_guard(main_program):
x = fluid.data(name='x', shape=self.x_shape)
y = functional.log_softmax(x, axis, dtype)
y = fluid.layers.log_softmax(x, axis, dtype)
exe = fluid.Executor(place)
out = exe.run(main_program, feed={'x': self.x}, fetch_list=[y])
self.assertTrue(np.allclose(out[0], ref_out))
with fluid.dygraph.guard(place):
x = fluid.dygraph.to_variable(self.x)
y = functional.log_softmax(x, axis, dtype)
y = fluid.layers.log_softmax(x, axis, dtype)
self.assertTrue(np.allclose(y.numpy(), ref_out))
def test_check_api(self):
......
python/paddle/fluid/tests/unittests/test_meshgrid_op.py
浏览文件 @ d3c9db75
......@@ -18,8 +18,8 @@ import unittest
import numpy as np
from op_test import OpTest, skip_check_grad_ci
import paddle.fluid as fluid
import paddle
from paddle.fluid import compiler, Program, program_guard, core
import paddle
class TestMeshgridOp(OpTest):
......@@ -79,7 +79,7 @@ class TestMeshgridOp3(unittest.TestCase):
out_2 = np.broadcast_to(out_2, [100, 200])
exe = fluid.Executor(place=fluid.CPUPlace())
grid_x, grid_y = paddle.tensor.meshgrid([x, y])
grid_x, grid_y = paddle.meshgrid([x, y])
res_1, res_2 = exe.run(fluid.default_main_program(),
feed={'x': input_1,
'y': input_2},
......@@ -95,7 +95,7 @@ class TestMeshgridOp4(unittest.TestCase):
def test_input_type():
x = fluid.data(shape=[200], dtype='float32', name='x2')
paddle.tensor.meshgrid(x)
paddle.meshgrid(x)
self.assertRaises(TypeError, test_input_type)
......@@ -108,7 +108,7 @@ class TestMeshgridOp5(unittest.TestCase):
with fluid.dygraph.guard():
tensor_3 = fluid.dygraph.to_variable(input_3)
tensor_4 = fluid.dygraph.to_variable(input_4)
res_3, res_4 = paddle.tensor.meshgrid([tensor_3, tensor_4])
res_3, res_4 = paddle.meshgrid([tensor_3, tensor_4])
assert np.array_equal(res_3.shape, [100, 200])
assert np.array_equal(res_4.shape, [100, 200])
......
python/paddle/fluid/tests/unittests/test_mse_loss.py
浏览文件 @ d3c9db75
......@@ -78,7 +78,7 @@ class TestNNMseLoss(unittest.TestCase):
name='input', shape=dim, dtype='float32')
label = fluid.layers.data(
name='label', shape=dim, dtype='float32')
mse_loss = paddle.nn.loss.MSELoss()
mse_loss = fluid.dygraph.MSELoss()
ret = mse_loss(input, label)
exe = fluid.Executor(place)
......@@ -89,7 +89,7 @@ class TestNNMseLoss(unittest.TestCase):
fetch_list=[ret])
with fluid.dygraph.guard():
mse_loss = paddle.nn.loss.MSELoss()
mse_loss = fluid.dygraph.MSELoss()
dy_ret = mse_loss(
fluid.dygraph.to_variable(input_np),
fluid.dygraph.to_variable(label_np))
......@@ -115,7 +115,7 @@ class TestNNMseLoss(unittest.TestCase):
name='input', shape=dim, dtype='float32')
label = fluid.layers.data(
name='label', shape=dim, dtype='float32')
mse_loss = paddle.nn.loss.MSELoss(reduction='sum')
mse_loss = fluid.dygraph.MSELoss(reduction='sum')
ret = mse_loss(input, label)
exe = fluid.Executor(place)
......@@ -126,7 +126,7 @@ class TestNNMseLoss(unittest.TestCase):
fetch_list=[ret])
with fluid.dygraph.guard():
mse_loss = paddle.nn.loss.MSELoss(reduction='sum')
mse_loss = fluid.dygraph.MSELoss(reduction='sum')
dy_ret = mse_loss(
fluid.dygraph.to_variable(input_np),
fluid.dygraph.to_variable(label_np))
......@@ -152,7 +152,7 @@ class TestNNMseLoss(unittest.TestCase):
name='input', shape=dim, dtype='float32')
label = fluid.layers.data(
name='label', shape=dim, dtype='float32')
mse_loss = paddle.nn.loss.MSELoss(reduction='none')
mse_loss = fluid.dygraph.MSELoss(reduction='none')
ret = mse_loss(input, label)
exe = fluid.Executor(place)
......@@ -163,7 +163,7 @@ class TestNNMseLoss(unittest.TestCase):
fetch_list=[ret])
with fluid.dygraph.guard():
mse_loss = paddle.nn.loss.MSELoss(reduction='none')
mse_loss = fluid.dygraph.MSELoss(reduction='none')
dy_ret = mse_loss(
fluid.dygraph.to_variable(input_np),
fluid.dygraph.to_variable(label_np))
......
python/paddle/fluid/tests/unittests/test_nll_loss.py
浏览文件 @ d3c9db75
......@@ -82,7 +82,7 @@ class TestNLLLoss(unittest.TestCase):
with fluid.program_guard(prog, startup_prog):
input = fluid.data(name='input', shape=[10, 10], dtype='float64')
label = fluid.data(name='label', shape=[10], dtype='int64')
nll_loss = paddle.nn.loss.NLLLoss()
nll_loss = fluid.dygraph.NLLLoss()
res = nll_loss(input, label)
exe = fluid.Executor(place)
......@@ -93,7 +93,7 @@ class TestNLLLoss(unittest.TestCase):
fetch_list=[res])
with fluid.dygraph.guard():
nll_loss = paddle.nn.loss.NLLLoss()
nll_loss = fluid.dygraph.NLLLoss()
dy_res = nll_loss(
fluid.dygraph.to_variable(input_np),
fluid.dygraph.to_variable(label_np))
......@@ -115,7 +115,7 @@ class TestNLLLoss(unittest.TestCase):
with fluid.program_guard(prog, startup_prog):
input = fluid.data(name='input', shape=[10, 10], dtype='float64')
label = fluid.data(name='label', shape=[10], dtype='int64')
nll_loss = paddle.nn.loss.NLLLoss(reduction='sum')
nll_loss = fluid.dygraph.NLLLoss(reduction='sum')
res = nll_loss(input, label)
exe = fluid.Executor(place)
......@@ -126,7 +126,7 @@ class TestNLLLoss(unittest.TestCase):
fetch_list=[res])
with fluid.dygraph.guard():
nll_loss = paddle.nn.loss.NLLLoss(reduction='sum')
nll_loss = fluid.dygraph.NLLLoss(reduction='sum')
dy_res = nll_loss(
fluid.dygraph.to_variable(input_np),
fluid.dygraph.to_variable(label_np))
......@@ -150,7 +150,7 @@ class TestNLLLoss(unittest.TestCase):
input = fluid.data(name='input', shape=[10, 10], dtype='float64')
label = fluid.data(name='label', shape=[10], dtype='int64')
weight = fluid.data(name='weight', shape=[10], dtype='float64')
nll_loss = paddle.nn.loss.NLLLoss(weight=weight)
nll_loss = fluid.dygraph.NLLLoss(weight=weight)
res = nll_loss(input, label)
exe = fluid.Executor(place)
......@@ -163,7 +163,7 @@ class TestNLLLoss(unittest.TestCase):
fetch_list=[res])
with fluid.dygraph.guard():
nll_loss = paddle.nn.loss.NLLLoss(
nll_loss = fluid.dygraph.NLLLoss(
weight=fluid.dygraph.to_variable(weight_np))
dy_res = nll_loss(
fluid.dygraph.to_variable(input_np),
......@@ -188,7 +188,7 @@ class TestNLLLoss(unittest.TestCase):
input = fluid.data(name='input', shape=[10, 10], dtype='float64')
label = fluid.data(name='label', shape=[10], dtype='int64')
weight = fluid.data(name='weight', shape=[10], dtype='float64')
nll_loss = paddle.nn.loss.NLLLoss(weight=weight, reduction='sum')
nll_loss = fluid.dygraph.NLLLoss(weight=weight, reduction='sum')
res = nll_loss(input, label)
exe = fluid.Executor(place)
......@@ -201,7 +201,7 @@ class TestNLLLoss(unittest.TestCase):
fetch_list=[res])
with fluid.dygraph.guard():
nll_loss = paddle.nn.loss.NLLLoss(
nll_loss = fluid.dygraph.NLLLoss(
weight=fluid.dygraph.to_variable(weight_np), reduction='sum')
dy_res = nll_loss(
fluid.dygraph.to_variable(input_np),
......@@ -225,7 +225,7 @@ class TestNLLLoss(unittest.TestCase):
input = fluid.data(name='input', shape=[10, 10], dtype='float64')
label = fluid.data(name='label', shape=[10], dtype='int64')
weight = fluid.data(name='weight', shape=[10], dtype='float64')
nll_loss = paddle.nn.loss.NLLLoss(weight=weight)
nll_loss = fluid.dygraph.NLLLoss(weight=weight)
res = nll_loss(input, label)
exe = fluid.Executor(place)
......@@ -238,7 +238,7 @@ class TestNLLLoss(unittest.TestCase):
fetch_list=[res])
with fluid.dygraph.guard():
nll_loss = paddle.nn.loss.NLLLoss(
nll_loss = fluid.dygraph.NLLLoss(
weight=fluid.dygraph.to_variable(weight_np))
dy_res = nll_loss(
fluid.dygraph.to_variable(input_np),
......@@ -261,7 +261,7 @@ class TestNLLLoss(unittest.TestCase):
input = fluid.data(name='input', shape=[10, 10], dtype='float64')
label = fluid.data(name='label', shape=[10], dtype='int64')
weight = fluid.data(name='weight', shape=[10], dtype='float64')
nll_loss = paddle.nn.loss.NLLLoss(weight=weight, reduction='none')
nll_loss = fluid.dygraph.NLLLoss(weight=weight, reduction='none')
res = nll_loss(input, label)
exe = fluid.Executor(place)
......@@ -274,7 +274,7 @@ class TestNLLLoss(unittest.TestCase):
fetch_list=[res])
with fluid.dygraph.guard():
nll_loss = paddle.nn.loss.NLLLoss(
nll_loss = fluid.dygraph.NLLLoss(
weight=fluid.dygraph.to_variable(weight_np), reduction='none')
dy_res = nll_loss(
fluid.dygraph.to_variable(input_np),
......@@ -299,7 +299,7 @@ class TestNLLLoss(unittest.TestCase):
input = fluid.data(
name='input', shape=[5, 3, 5, 5], dtype='float64')
label = fluid.data(name='label', shape=[5, 5, 5], dtype='int64')
nll_loss = paddle.nn.loss.NLLLoss()
nll_loss = fluid.dygraph.NLLLoss()
res = nll_loss(input, label)
exe = fluid.Executor(place)
......@@ -310,7 +310,7 @@ class TestNLLLoss(unittest.TestCase):
fetch_list=[res])
with fluid.dygraph.guard():
nll_loss = paddle.nn.loss.NLLLoss()
nll_loss = fluid.dygraph.NLLLoss()
dy_res = nll_loss(
fluid.dygraph.to_variable(input_np),
fluid.dygraph.to_variable(label_np))
......@@ -334,7 +334,7 @@ class TestNLLLoss(unittest.TestCase):
input = fluid.data(
name='input', shape=[5, 3, 5, 5], dtype='float64')
label = fluid.data(name='label', shape=[5, 5, 5], dtype='int64')
nll_loss = paddle.nn.loss.NLLLoss(reduction='sum')
nll_loss = fluid.dygraph.NLLLoss(reduction='sum')
res = nll_loss(input, label)
exe = fluid.Executor(place)
......@@ -345,7 +345,7 @@ class TestNLLLoss(unittest.TestCase):
fetch_list=[res])
with fluid.dygraph.guard():
nll_loss = paddle.nn.loss.NLLLoss(reduction='sum')
nll_loss = fluid.dygraph.NLLLoss(reduction='sum')
dy_res = nll_loss(
fluid.dygraph.to_variable(input_np),
fluid.dygraph.to_variable(label_np))
......@@ -372,7 +372,7 @@ class TestNLLLoss(unittest.TestCase):
label = fluid.data(name='label', shape=[5, 5, 5], dtype='int64')
weight = fluid.data(name='weight', shape=[3], dtype='float64')
nll_loss = paddle.nn.loss.NLLLoss(weight=weight)
nll_loss = fluid.dygraph.NLLLoss(weight=weight)
res = nll_loss(input, label)
exe = fluid.Executor(place)
......@@ -385,7 +385,7 @@ class TestNLLLoss(unittest.TestCase):
fetch_list=[res])
with fluid.dygraph.guard():
nll_loss = paddle.nn.loss.NLLLoss(
nll_loss = fluid.dygraph.NLLLoss(
weight=fluid.dygraph.to_variable(weight_np))
dy_res = nll_loss(
fluid.dygraph.to_variable(input_np),
......@@ -411,7 +411,7 @@ class TestNLLLoss(unittest.TestCase):
label = fluid.data(name='label', shape=[5, 5, 5], dtype='int64')
weight = fluid.data(name='weight', shape=[3], dtype='float64')
nll_loss = paddle.nn.loss.NLLLoss(weight=weight)
nll_loss = fluid.dygraph.NLLLoss(weight=weight)
res = nll_loss(input, label)
exe = fluid.Executor(place)
......@@ -424,7 +424,7 @@ class TestNLLLoss(unittest.TestCase):
fetch_list=[res])
with fluid.dygraph.guard():
nll_loss = paddle.nn.loss.NLLLoss(
nll_loss = fluid.dygraph.NLLLoss(
weight=fluid.dygraph.to_variable(weight_np))
dy_res = nll_loss(
fluid.dygraph.to_variable(input_np),
......@@ -452,7 +452,7 @@ class TestNLLLoss(unittest.TestCase):
label = fluid.data(name='label', shape=[5, 5, 5], dtype='int64')
weight = fluid.data(name='weight', shape=[3], dtype='float64')
nll_loss = paddle.nn.loss.NLLLoss(weight=weight, reduction='sum')
nll_loss = fluid.dygraph.NLLLoss(weight=weight, reduction='sum')
res = nll_loss(input, label)
exe = fluid.Executor(place)
......@@ -465,7 +465,7 @@ class TestNLLLoss(unittest.TestCase):
fetch_list=[res])
with fluid.dygraph.guard():
nll_loss = paddle.nn.loss.NLLLoss(
nll_loss = fluid.dygraph.NLLLoss(
weight=fluid.dygraph.to_variable(weight_np), reduction='sum')
dy_res = nll_loss(
fluid.dygraph.to_variable(input_np),
......@@ -491,7 +491,7 @@ class TestNLLLoss(unittest.TestCase):
input = fluid.data(
name='input', shape=[5, 3, 5, 5, 5], dtype='float64')
label = fluid.data(name='label', shape=[5, 5, 5, 5], dtype='int64')
nll_loss = paddle.nn.loss.NLLLoss()
nll_loss = fluid.dygraph.NLLLoss()
res = nll_loss(input, label)
exe = fluid.Executor(place)
......@@ -502,7 +502,7 @@ class TestNLLLoss(unittest.TestCase):
fetch_list=[res])
with fluid.dygraph.guard():
nll_loss = paddle.nn.loss.NLLLoss()
nll_loss = fluid.dygraph.NLLLoss()
dy_res = nll_loss(
fluid.dygraph.to_variable(input_np),
fluid.dygraph.to_variable(label_np))
......@@ -533,7 +533,7 @@ class TestNLLLoss(unittest.TestCase):
name='input', shape=[5, 3, 5, 5, 5], dtype='float64')
label = fluid.data(name='label', shape=[5, 5, 5, 5], dtype='int64')
weight = fluid.data(name='weight', shape=[3], dtype='float64')
nll_loss = paddle.nn.loss.NLLLoss(weight=weight)
nll_loss = fluid.dygraph.NLLLoss(weight=weight)
res = nll_loss(input, label)
exe = fluid.Executor(place)
......@@ -546,7 +546,7 @@ class TestNLLLoss(unittest.TestCase):
fetch_list=[res])
with fluid.dygraph.guard():
nll_loss = paddle.nn.loss.NLLLoss(
nll_loss = fluid.dygraph.NLLLoss(
weight=fluid.dygraph.to_variable(weight_np))
dy_res = nll_loss(
fluid.dygraph.to_variable(input_np),
......@@ -579,7 +579,7 @@ class TestNLLLoss(unittest.TestCase):
name='input', shape=[5, 3, 5, 5, 5], dtype='float64')
label = fluid.data(name='label', shape=[5, 5, 5, 5], dtype='int64')
weight = fluid.data(name='weight', shape=[3], dtype='float64')
nll_loss = paddle.nn.loss.NLLLoss(weight=weight, reduction='sum')
nll_loss = fluid.dygraph.NLLLoss(weight=weight, reduction='sum')
res = nll_loss(input, label)
exe = fluid.Executor(place)
......@@ -592,7 +592,7 @@ class TestNLLLoss(unittest.TestCase):
fetch_list=[res])
with fluid.dygraph.guard():
nll_loss = paddle.nn.loss.NLLLoss(
nll_loss = fluid.dygraph.NLLLoss(
weight=fluid.dygraph.to_variable(weight_np), reduction='sum')
dy_res = nll_loss(
fluid.dygraph.to_variable(input_np),
......@@ -628,7 +628,7 @@ class TestNLLLoss(unittest.TestCase):
name='input', shape=[5, 3, 5, 5, 5], dtype='float64')
label = fluid.data(name='label', shape=[5, 5, 5, 5], dtype='int64')
weight = fluid.data(name='weight', shape=[3], dtype='float64')
nll_loss = paddle.nn.loss.NLLLoss(weight=weight, reduction='none')
nll_loss = fluid.dygraph.NLLLoss(weight=weight, reduction='none')
res = nll_loss(input, label)
exe = fluid.Executor(place)
......@@ -641,7 +641,7 @@ class TestNLLLoss(unittest.TestCase):
fetch_list=[res])
with fluid.dygraph.guard():
nll_loss = paddle.nn.loss.NLLLoss(
nll_loss = fluid.dygraph.NLLLoss(
weight=fluid.dygraph.to_variable(weight_np), reduction='none')
dy_res = nll_loss(
fluid.dygraph.to_variable(input_np),
......@@ -676,7 +676,7 @@ class TestNLLLoss(unittest.TestCase):
name='input', shape=[5, 3, 5, 5, 5], dtype='float64')
label = fluid.data(name='label', shape=[5, 5, 5, 5], dtype='int64')
weight = fluid.data(name='weight', shape=[3], dtype='float64')
nll_loss = paddle.nn.loss.NLLLoss(weight=weight, reduction='none')
nll_loss = fluid.dygraph.NLLLoss(weight=weight, reduction='none')
res = nll_loss(input, label)
exe = fluid.Executor(place)
......@@ -689,7 +689,7 @@ class TestNLLLoss(unittest.TestCase):
fetch_list=[res])
with fluid.dygraph.guard():
nll_loss = paddle.nn.loss.NLLLoss(
nll_loss = fluid.dygraph.NLLLoss(
weight=fluid.dygraph.to_variable(weight_np), reduction='none')
dy_res = nll_loss(
fluid.dygraph.to_variable(input_np),
......
python/paddle/fluid/tests/unittests/test_randint_op.py
浏览文件 @ d3c9db75
......@@ -22,7 +22,6 @@ import paddle.fluid.core as core
from paddle.fluid.op import Operator
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
import paddle
def output_hist(out):
......@@ -62,17 +61,18 @@ class TestRandintOpError(unittest.TestCase):
def test_shape():
shape = np.array([2, 3])
paddle.randint(5, shape=shape, dtype='int32')
fluid.layers.randint(5, shape=shape, dtype='int32')
self.assertRaises(TypeError, test_shape)
def test_dtype():
paddle.randint(5, shape=[32, 32], dtype='float32')
fluid.layers.randint(5, shape=[32, 32], dtype='float32')
self.assertRaises(TypeError, test_dtype)
def test_low_high():
paddle.randint(low=5, high=5, shape=[32, 32], dtype='int32')
fluid.layers.randint(
low=5, high=5, shape=[32, 32], dtype='int32')
self.assertRaises(ValueError, test_low_high)
......@@ -131,21 +131,21 @@ class TestRandintAPI(unittest.TestCase):
train_program = fluid.Program()
with fluid.program_guard(train_program, startup_program):
# results are from [0, 5).
output1 = paddle.randint(5)
output1 = fluid.layers.randint(5)
# shape is a list and dtype is 'int32'
output2 = paddle.randint(
output2 = fluid.layers.randint(
low=-100, high=100, shape=[64, 64], dtype='int32')
# shape is a tuple and dtype is 'int64'
output3 = paddle.randint(
output3 = fluid.layers.randint(
low=-100, high=100, shape=(32, 32, 3), dtype='int64')
# shape is a tensorlist and dtype is 'float32'
dim_1 = fluid.layers.fill_constant([1], "int64", 32)
dim_2 = fluid.layers.fill_constant([1], "int32", 50)
output4 = paddle.randint(
output4 = fluid.layers.randint(
low=-100, high=100, shape=[dim_1, 5], dtype='int32')
# shape is a tensor and dtype is 'float64'
var_shape = fluid.data(name='var_shape', shape=[2], dtype="int64")
output5 = paddle.randint(
output5 = fluid.layers.randint(
low=1, high=1000, shape=var_shape, dtype='int64')
place = fluid.CPUPlace()
......@@ -163,7 +163,7 @@ class TestRandintAPI(unittest.TestCase):
class TestRandintDygraphMode(unittest.TestCase):
def test_check_output(self):
with fluid.dygraph.guard():
x = paddle.randint(10, shape=[10], dtype="int32")
x = fluid.layers.randint(10, shape=[10], dtype="int32")
x_np = x.numpy()
for i in range(10):
self.assertTrue((x_np[i] >= 0 and x_np[i] < 10))
......
python/paddle/fluid/tests/unittests/test_randn_op.py
浏览文件 @ d3c9db75
......@@ -16,7 +16,6 @@ from __future__ import print_function
import unittest
import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid import Program, program_guard
......@@ -24,14 +23,16 @@ from paddle.fluid import Program, program_guard
class TestRandnOp(unittest.TestCase):
def test_api(self):
x1 = paddle.randn(shape=[1000, 784], dtype='float32')
x2 = paddle.randn(shape=[1000, 784], dtype='float64')
x1 = fluid.layers.randn(shape=[1000, 784], dtype='float32')
x2 = fluid.layers.randn(shape=[1000, 784], dtype='float64')
x3 = fluid.layers.fill_constant(
shape=[1000, 784], dtype='float32', value=0)
paddle.randn(shape=[1000, 784], out=x3, dtype='float32')
x4 = paddle.randn(shape=[1000, 784], dtype='float32', device='cpu')
x5 = paddle.randn(shape=[1000, 784], dtype='float32', device='gpu')
x6 = paddle.randn(
fluid.layers.randn(shape=[1000, 784], out=x3, dtype='float32')
x4 = fluid.layers.randn(
shape=[1000, 784], dtype='float32', device='cpu')
x5 = fluid.layers.randn(
shape=[1000, 784], dtype='float32', device='gpu')
x6 = fluid.layers.randn(
shape=[1000, 784],
dtype='float32',
device='gpu',
......@@ -64,43 +65,43 @@ class TestRandnOpError(unittest.TestCase):
# The argument shape's size of randn_op should not be 0.
def test_shape_size():
out = paddle.randn(shape=[])
out = fluid.layers.randn(shape=[])
self.assertRaises(AssertionError, test_shape_size)
# The argument shape's type of randn_op should be list or tuple.
def test_shape_type():
out = paddle.randn(shape=1)
out = fluid.layers.randn(shape=1)
self.assertRaises(TypeError, test_shape_type)
# The argument dtype of randn_op should be float32 or float64.
def test_dtype_float16():
out = paddle.randn(shape=[1, 2], dtype='float16')
out = fluid.layers.randn(shape=[1, 2], dtype='float16')
self.assertRaises(TypeError, test_dtype_float16)
# The argument dtype of randn_op should be float32 or float64.
def test_dtype_int32():
out = paddle.randn(shape=[1, 2], dtype='int32')
out = fluid.layers.randn(shape=[1, 2], dtype='int32')
self.assertRaises(TypeError, test_dtype_int32)
# The argument dtype of randn_op should be float32 or float64.
def test_dtype_int64():
out = paddle.randn(shape=[1, 2], dtype='int64')
out = fluid.layers.randn(shape=[1, 2], dtype='int64')
self.assertRaises(TypeError, test_dtype_int64)
# The argument dtype of randn_op should be float32 or float64.
def test_dtype_uint8():
out = paddle.randn(shape=[1, 2], dtype='uint8')
out = fluid.layers.randn(shape=[1, 2], dtype='uint8')
self.assertRaises(TypeError, test_dtype_uint8)
# The argument dtype of randn_op should be float32 or float64.
def test_dtype_bool():
out = paddle.randn(shape=[1, 2], dtype='bool')
out = fluid.layers.randn(shape=[1, 2], dtype='bool')
self.assertRaises(TypeError, test_dtype_bool)
......
python/paddle/fluid/tests/unittests/test_randperm_op.py
浏览文件 @ d3c9db75
......@@ -15,7 +15,6 @@
import unittest
import numpy as np
from op_test import OpTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.op import Operator
......@@ -120,12 +119,12 @@ class TestRandpermOpError(unittest.TestCase):
def test_Variable():
out = np.arange(10)
paddle.randperm(n=10, out=out)
fluid.layers.randperm(n=10, out=out)
self.assertRaises(TypeError, test_Variable)
def test_value():
paddle.randperm(n=-3)
fluid.layers.randperm(n=-3)
self.assertRaises(ValueError, test_value)
......@@ -139,9 +138,9 @@ class TestRandpermOp_attr_out(unittest.TestCase):
with fluid.program_guard(train_program, startup_program):
n = 10
data_1 = fluid.layers.fill_constant([n], "int64", 3)
paddle.randperm(n=n, out=data_1)
fluid.layers.randperm(n=n, out=data_1)
data_2 = paddle.randperm(n=n, dtype="int32", device="cpu")
data_2 = fluid.layers.randperm(n=n, dtype="int32", device="cpu")
place = fluid.CPUPlace()
if fluid.core.is_compiled_with_cuda():
......@@ -160,12 +159,12 @@ class TestRandpermDygraphMode(unittest.TestCase):
def test_check_output(self):
with fluid.dygraph.guard():
n = 10
data_1 = paddle.randperm(n, dtype="int64")
data_1 = fluid.layers.randperm(n, dtype="int64")
data_1_np = data_1.numpy()
self.assertTrue(
check_randperm_out(n, data_1_np), msg=error_msg(data_1_np))
data_2 = paddle.randperm(n, dtype="int32", device="cpu")
data_2 = fluid.layers.randperm(n, dtype="int32", device="cpu")
data_2_np = data_2.numpy()
self.assertTrue(
check_randperm_out(n, data_2_np), msg=error_msg(data_2_np))
......
python/paddle/fluid/tests/unittests/test_roll_op.py
浏览文件 @ d3c9db75
......@@ -15,7 +15,6 @@
from __future__ import print_function
import unittest
import paddle
import numpy as np
import paddle.fluid.core as core
from op_test import OpTest
......@@ -66,7 +65,7 @@ class TestRollAPI(unittest.TestCase):
# case 1:
with program_guard(Program(), Program()):
x = fluid.layers.data(name='x', shape=[-1, 3])
z = paddle.roll(x, shifts=1)
z = fluid.layers.roll(x, shifts=1)
exe = fluid.Executor(fluid.CPUPlace())
res, = exe.run(feed={'x': self.data_x},
fetch_list=[z.name],
......@@ -78,7 +77,7 @@ class TestRollAPI(unittest.TestCase):
# case 2:
with program_guard(Program(), Program()):
x = fluid.layers.data(name='x', shape=[-1, 3])
z = paddle.roll(x, shifts=1, dims=0)
z = fluid.layers.roll(x, shifts=1, dims=0)
exe = fluid.Executor(fluid.CPUPlace())
res, = exe.run(feed={'x': self.data_x},
fetch_list=[z.name],
......@@ -92,7 +91,7 @@ class TestRollAPI(unittest.TestCase):
# case 1:
with fluid.dygraph.guard():
x = fluid.dygraph.to_variable(self.data_x)
z = paddle.roll(x, shifts=1)
z = fluid.layers.roll(x, shifts=1)
np_z = z.numpy()
expect_out = np.array([[9.0, 1.0, 2.0], [3.0, 4.0, 5.0],
[6.0, 7.0, 8.0]])
......@@ -101,7 +100,7 @@ class TestRollAPI(unittest.TestCase):
# case 2:
with fluid.dygraph.guard():
x = fluid.dygraph.to_variable(self.data_x)
z = paddle.roll(x, shifts=1, dims=0)
z = fluid.layers.roll(x, shifts=1, dims=0)
np_z = z.numpy()
expect_out = np.array([[7.0, 8.0, 9.0], [1.0, 2.0, 3.0],
[4.0, 5.0, 6.0]])
......
python/paddle/fluid/tests/unittests/test_tril_triu_op.py
浏览文件 @ d3c9db75
......@@ -17,7 +17,6 @@ import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid as fluid
import paddle.tensor as tensor
class TrilTriuOpDefaultTest(OpTest):
......@@ -71,7 +70,7 @@ def case_generator(op_type, Xshape, diagonal, expected):
data = fluid.data(shape=Xshape, dtype='float64', name=cls_name)
with self.assertRaisesRegexp(
eval(expected.split(':')[-1]), errmsg[expected]):
getattr(tensor, op_type)(input=data, diagonal=diagonal)
getattr(fluid.layers, op_type)(input=data, diagonal=diagonal)
class SuccessCase(TrilTriuOpDefaultTest):
def initTestCase(self):
......@@ -122,7 +121,7 @@ class TestTrilTriuOpAPI(unittest.TestCase):
def test_api(self):
data = np.random.random([1, 9, 9, 4]).astype('float32')
x = fluid.data(shape=[1, 9, -1, 4], dtype='float32', name='x')
tril_out, triu_out = tensor.tril(x), tensor.triu(x)
tril_out, triu_out = fluid.layers.tril(x), fluid.layers.triu(x)
place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
) else fluid.CPUPlace()
......
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