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Paddle
未验证 提交 99408718 编写于 作者: C cnn 提交者： GitHub
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[cherry pick ] cherry pick 28108 28198 28199 from release2.0rc (#28215) 

* Release 2.0rc cherry pick api rename #28108 (#28184)

* rename count_include_pad-->exclusive  return_indices-->return_mask

* remove track_running_stats

* fix typo.

* rename xxxd-->xxxxD

* solve conflicts

* 2.0rc api add all any (#28199)

* reduce trt warning message (#28011)

add paddle.enable_static() on sample code

alias recude_all-->all, reduce_any-->any

add import reduce_all and reduce_any in python/paddle/tensor/math.py

import all and any in python/paddle/tensor/__init__.py

remove all and any OP in python/paddle/tensor/logic.py, add all and any OP in python/paddle/tensor/math.py

fix import error

remove TestAllAPI temporary

* fix doc of recdue_all and reduce_any, test=document_fix

* fix typo

* fix unittest for all and any API
Co-authored-by: NPei Yang <peiyang@baidu.com>

* rename conv_transposeXd-->convXd_transpose (#28198)

* fix sample code of reduce_all and reduce_any
Co-authored-by: NPei Yang <peiyang@baidu.com>
上级 d2522197
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Showing with 542 addition and 233 deletion
python/paddle/__init__.py
浏览文件 @ 99408718
......@@ -103,8 +103,6 @@ from .tensor.logic import logical_not #DEFINE_ALIAS
from .tensor.logic import logical_or #DEFINE_ALIAS
from .tensor.logic import logical_xor #DEFINE_ALIAS
from .tensor.logic import not_equal #DEFINE_ALIAS
# from .tensor.logic import reduce_all #DEFINE_ALIAS
# from .tensor.logic import reduce_any #DEFINE_ALIAS
from .tensor.logic import allclose #DEFINE_ALIAS
from .tensor.logic import equal_all #DEFINE_ALIAS
# from .tensor.logic import isnan #DEFINE_ALIAS
......@@ -162,6 +160,8 @@ from .tensor.math import reciprocal #DEFINE_ALIAS
# from .tensor.math import reduce_min #DEFINE_ALIAS
# from .tensor.math import reduce_prod #DEFINE_ALIAS
# from .tensor.math import reduce_sum #DEFINE_ALIAS
from .tensor.math import all #DEFINE_ALIAS
from .tensor.math import any #DEFINE_ALIAS
from .tensor.math import round #DEFINE_ALIAS
from .tensor.math import rsqrt #DEFINE_ALIAS
from .tensor.math import scale #DEFINE_ALIAS
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 99408718
......@@ -315,6 +315,8 @@ def fc(input,
.. code-block:: python
import paddle.fluid as fluid
import paddle
paddle.enable_static()
# when input is single tensor
data = fluid.data(name="data", shape=[-1, 32], dtype="float32")
fc = fluid.layers.fc(input=data, size=1000, act="tanh")
......@@ -468,6 +470,9 @@ def embedding(input,
import paddle.fluid as fluid
import numpy as np
import paddle
paddle.enable_static()
data = fluid.data(name='x', shape=[None, 1], dtype='int64')
# example 1
......@@ -731,6 +736,8 @@ def linear_chain_crf(input, label, param_attr=None, length=None):
import paddle.fluid as fluid
import numpy as np
import paddle
paddle.enable_static()
#define net structure, using LodTensor
train_program = fluid.Program()
......@@ -855,6 +862,8 @@ def crf_decoding(input, param_attr, label=None, length=None):
.. code-block:: python
import paddle.fluid as fluid
import paddle
paddle.enable_static()
# LoDTensor-based example
num_labels = 10
......@@ -1458,6 +1467,9 @@ def conv2d(input,
.. code-block:: python
import paddle.fluid as fluid
import paddle
paddle.enable_static()
data = fluid.data(name='data', shape=[None, 3, 32, 32], dtype='float32')
conv2d = fluid.layers.conv2d(input=data, num_filters=2, filter_size=3, act="relu")
"""
......@@ -1728,6 +1740,8 @@ def conv3d(input,
.. code-block:: python
import paddle.fluid as fluid
import paddle
paddle.enable_static()
data = fluid.data(name='data', shape=[None, 3, 12, 32, 32], dtype='float32')
conv3d = fluid.layers.conv3d(input=data, num_filters=2, filter_size=3, act="relu")
"""
......@@ -2377,6 +2391,7 @@ def adaptive_pool2d(input,
# output[:, :, i, j] = avg(input[:, :, hstart: hend, wstart: wend])
#
import paddle
paddle.enable_static()
data = paddle.rand(shape=[1,3,32,32])
pool_out = paddle.fluid.layers.adaptive_pool2d(
input=data,
......@@ -2531,6 +2546,7 @@ def adaptive_pool3d(input,
#
import paddle
paddle.enable_static()
data = paddle.rand(shape=[1,3,32,32,32])
pool_out = paddle.fluid.layers.adaptive_pool3d(
input=data,
......@@ -2726,6 +2742,8 @@ def batch_norm(input,
.. code-block:: python
import paddle.fluid as fluid
import paddle
paddle.enable_static()
x = fluid.data(name='x', shape=[3, 7, 3, 7], dtype='float32')
hidden1 = fluid.layers.fc(input=x, size=200, param_attr='fc1.w')
hidden2 = fluid.layers.batch_norm(input=hidden1)
......@@ -2735,6 +2753,8 @@ def batch_norm(input,
# batch_norm with momentum as Variable
import paddle.fluid as fluid
import paddle.fluid.layers.learning_rate_scheduler as lr_scheduler
import paddle
paddle.enable_static()
def get_decay_momentum(momentum_init, decay_steps, decay_rate):
global_step = lr_scheduler._decay_step_counter()
......@@ -3134,6 +3154,8 @@ def instance_norm(input,
.. code-block:: python
import paddle.fluid as fluid
import paddle
paddle.enable_static()
x = fluid.data(name='x', shape=[3, 7, 3, 7], dtype='float32')
hidden1 = fluid.layers.fc(input=x, size=200, param_attr='fc1.w')
hidden2 = fluid.layers.instance_norm(input=hidden1)
......@@ -3269,6 +3291,7 @@ def data_norm(input,
.. code-block:: python
import paddle
paddle.enable_static()
x = paddle.randn(shape=[32,100])
hidden2 = paddle.static.nn.data_norm(input=x)
......@@ -3451,6 +3474,8 @@ def layer_norm(input,
import paddle.fluid as fluid
import numpy as np
import paddle
paddle.enable_static()
x = fluid.data(name='x', shape=[-1, 32, 32], dtype='float32')
hidden1 = fluid.layers.layer_norm(input=x, begin_norm_axis=1)
place = fluid.CPUPlace()
......@@ -3566,6 +3591,9 @@ def group_norm(input,
.. code-block:: python
import paddle.fluid as fluid
import paddle
paddle.enable_static()
data = fluid.data(name='data', shape=[None, 8, 32, 32], dtype='float32')
x = fluid.layers.group_norm(input=data, groups=4)
"""
......@@ -3887,6 +3915,8 @@ def conv2d_transpose(input,
.. code-block:: python
import paddle.fluid as fluid
import paddle
paddle.enable_static()
data = fluid.data(name='data', shape=[None, 3, 32, 32], dtype='float32')
conv2d_transpose = fluid.layers.conv2d_transpose(input=data, num_filters=2, filter_size=3)
"""
......@@ -4177,6 +4207,8 @@ def conv3d_transpose(input,
.. code-block:: python
import paddle.fluid as fluid
import paddle
paddle.enable_static()
data = fluid.data(name='data', shape=[None, 3, 12, 32, 32], dtype='float32')
conv3d_transpose = fluid.layers.conv3d_transpose(input=data, num_filters=2, filter_size=3)
"""
......@@ -4659,7 +4691,7 @@ def reduce_all(input, dim=None, keep_dim=False, name=None):
This OP computes the ``logical and`` of tensor elements over the given dimension, and output the result.
Args:
input (Variable): The input variable which is a Tensor or LoDTensor, the input data type should be `bool`.
input (Tensor): the input tensor, it's data type should be `bool`.
dim (list|int|optional): The dimension along which the logical and is computed.
If :attr:`None`, compute the logical and over all elements of
:attr:`input` and return a Tensor variable with a single element,
......@@ -4672,11 +4704,12 @@ def reduce_all(input, dim=None, keep_dim=False, name=None):
will be named automatically. The default value is None.
Returns:
Variable, the output data type is bool. : The reduced tensor variable with ``logical and`` in given dims.
Tensor, the output data type is bool. : The reduced tensor variable with ``logical and`` in given dims.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import numpy as np
......@@ -4684,15 +4717,15 @@ def reduce_all(input, dim=None, keep_dim=False, name=None):
# x is a bool Tensor variable with following elements:
# [[True, False]
# [True, True]]
x = layers.assign(np.array([[1, 0], [1, 1]], dtype='int32'))
x = layers.cast(x, 'bool')
x = fluid.layers.assign(np.array([[1, 0], [1, 1]], dtype='int32'))
x = fluid.layers.cast(x, 'bool')
out = layers.reduce_all(x) # False
out = layers.reduce_all(x, dim=0) # [True, False]
out = layers.reduce_all(x, dim=-1) # [False, True]
out = fluid.layers.reduce_all(x) # False
out = fluid.layers.reduce_all(x, dim=0) # [True, False]
out = fluid.layers.reduce_all(x, dim=-1) # [False, True]
# keep_dim=False, x.shape=(2,2), out.shape=(2,)
out = layers.reduce_all(x, dim=1, keep_dim=True) # [[False], [True]]
out = fluid.layers.reduce_all(x, dim=1, keep_dim=True) # [[False], [True]]
# keep_dim=True, x.shape=(2,2), out.shape=(2,1)
"""
......@@ -4719,7 +4752,7 @@ def reduce_any(input, dim=None, keep_dim=False, name=None):
This OP computes the ``logical or`` of tensor elements over the given dimension, and output the result.
Args:
input (Variable): The input variable which is a Tensor or LoDTensor, the input data type should be `bool`.
input (Tensor): the input tensor, it's data type should be `bool`.
dim (list|int|optional): The dimension along which the logical and is computed.
If :attr:`None`, compute the logical and over all elements of
:attr:`input` and return a Tensor variable with a single element,
......@@ -4728,14 +4761,15 @@ def reduce_any(input, dim=None, keep_dim=False, name=None):
keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true. The default value is False.
name(str|None): A name for this layer(optional). If set None, the layer
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
Variable, the output data type is bool. : The reduced tensor variable with ``logical or`` in given dims.
Tensor, the output data type is bool. : The reduced tensor variable with ``logical or`` in given dims.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import numpy as np
......@@ -4743,15 +4777,15 @@ def reduce_any(input, dim=None, keep_dim=False, name=None):
# x is a bool Tensor variable with following elements:
# [[True, False]
# [False, False]]
x = layers.assign(np.array([[1, 0], [0, 0]], dtype='int32'))
x = layers.cast(x, 'bool')
x = fluid.layers.assign(np.array([[1, 0], [0, 0]], dtype='int32'))
x = fluid.layers.cast(x, 'bool')
out = layers.reduce_any(x) # True
out = layers.reduce_any(x, dim=0) # [True, False]
out = layers.reduce_any(x, dim=-1) # [True, False]
out = fluid.layers.reduce_any(x) # True
out = fluid.layers.reduce_any(x, dim=0) # [True, False]
out = fluid.layers.reduce_any(x, dim=-1) # [True, False]
# keep_dim=False, x.shape=(2,2), out.shape=(2,)
out = layers.reduce_any(x, dim=1,
out = fluid.layers.reduce_any(x, dim=1,
keep_dim=True) # [[True], [False]]
# keep_dim=True, x.shape=(2,2), out.shape=(2,1)
......@@ -5613,6 +5647,8 @@ def im2sequence(input,
.. code-block:: python
import paddle.fluid as fluid
import paddle
paddle.enable_static()
data = fluid.data(name='data', shape=[None, 3, 32, 32],
dtype='float32')
output = fluid.layers.im2sequence(
......@@ -5669,6 +5705,8 @@ def row_conv(input, future_context_size, param_attr=None, act=None):
Examples:
>>> # for LodTensor inputs
>>> import paddle.fluid as fluid
>>> import paddle
>>> paddle.enable_static()
>>> x = fluid.data(name='x', shape=[9, 16],
>>> dtype='float32', lod_level=1)
>>> out = fluid.layers.row_conv(input=x, future_context_size=2)
......@@ -5982,6 +6020,8 @@ def autoincreased_step_counter(counter_name=None, begin=1, step=1):
.. code-block:: python
import paddle.fluid as fluid
import paddle
paddle.enable_static()
global_step = fluid.layers.autoincreased_step_counter(
counter_name='@LR_DECAY_COUNTER@', begin=0, step=1)
"""
......@@ -9730,6 +9770,8 @@ def prelu(x, mode, param_attr=None, name=None):
.. code-block:: python
import paddle.fluid as fluid
import paddle
paddle.enable_static()
from paddle.fluid.param_attr import ParamAttr
x = fluid.data(name="x", shape=[None,5,10,10], dtype="float32")
mode = 'channel'
......@@ -14307,6 +14349,9 @@ def deformable_conv(input,
#deformable conv v2:
import paddle.fluid as fluid
import paddle
paddle.enable_static()
C_in, H_in, W_in = 3, 32, 32
filter_size, deformable_groups = 3, 1
data = fluid.data(name='data', shape=[None, C_in, H_in, W_in], dtype='float32')
......
python/paddle/fluid/tests/unittests/parallel_dygraph_sync_batch_norm.py
浏览文件 @ 99408718
......@@ -63,10 +63,7 @@ class TestLayer(fluid.dygraph.Layer):
bias_attr=False)
self._sync_batch_norm2 = SyncBatchNorm(
num_filters,
weight_attr=False,
bias_attr=False,
track_running_stats=False)
num_filters, weight_attr=False, bias_attr=False)
def forward(self, inputs):
y = self._conv(inputs)
......
python/paddle/fluid/tests/unittests/test_adaptive_max_pool2d.py
浏览文件 @ 99408718
......@@ -150,7 +150,7 @@ class TestAdaptiveMaxPool2DAPI(unittest.TestCase):
x = paddle.to_tensor(self.x_np)
out_1 = paddle.nn.functional.adaptive_max_pool2d(
x=x, return_indices=False, output_size=[3, 3])
x=x, return_mask=False, output_size=[3, 3])
out_2 = paddle.nn.functional.adaptive_max_pool2d(x=x, output_size=5)
......
python/paddle/fluid/tests/unittests/test_conv1d_transpose_layer.py
浏览文件 @ 99408718
......@@ -92,7 +92,7 @@ class Conv1DTransposeTestCase(unittest.TestCase):
"weight", self.weight_shape, dtype=self.dtype)
b_var = fluid.data(
"bias", (self.out_channels, ), dtype=self.dtype)
y_var = F.conv_transpose1d(
y_var = F.conv1d_transpose(
x_var,
w_var,
None if self.no_bias else b_var,
......
python/paddle/fluid/tests/unittests/test_conv2d_transpose_layer.py
浏览文件 @ 99408718
......@@ -128,7 +128,7 @@ class Conv2DTransposeTestCase(unittest.TestCase):
else:
output_size = self.output_size
y_var = F.conv_transpose2d(
y_var = F.conv2d_transpose(
x_var,
w_var,
None if self.no_bias else b_var,
......
python/paddle/fluid/tests/unittests/test_conv3d_transpose_layer.py
浏览文件 @ 99408718
......@@ -119,7 +119,7 @@ class Conv3DTransposeTestCase(unittest.TestCase):
"weight", self.weight_shape, dtype=self.dtype)
b_var = fluid.data(
"bias", (self.num_filters, ), dtype=self.dtype)
y_var = F.conv_transpose3d(
y_var = F.conv3d_transpose(
x_var,
w_var,
None if self.no_bias else b_var,
......
python/paddle/fluid/tests/unittests/test_functional_conv2d_transpose.py
浏览文件 @ 99408718
......@@ -111,7 +111,7 @@ class TestFunctionalConv2D(TestCase):
"weight", self.weight.shape, dtype=self.dtype)
if not self.no_bias:
bias = fluid.data("bias", self.bias.shape, dtype=self.dtype)
y = F.conv_transpose2d(
y = F.conv2d_transpose(
x,
weight,
None if self.no_bias else bias,
......@@ -134,7 +134,7 @@ class TestFunctionalConv2D(TestCase):
x = dg.to_variable(self.input)
weight = dg.to_variable(self.weight)
bias = None if self.no_bias else dg.to_variable(self.bias)
y = F.conv_transpose2d(
y = F.conv2d_transpose(
x,
weight,
bias,
......@@ -215,7 +215,7 @@ class TestFunctionalConv2DError(TestCase):
"weight", self.weight_shape, dtype=self.dtype)
if not self.no_bias:
bias = fluid.data("bias", self.bias_shape, dtype=self.dtype)
y = F.conv_transpose2d(
y = F.conv2d_transpose(
x,
weight,
None if self.no_bias else bias,
......
python/paddle/fluid/tests/unittests/test_functional_conv3d_transpose.py
浏览文件 @ 99408718
......@@ -113,7 +113,7 @@ class TestFunctionalConv3DTranspose(TestCase):
"weight", self.weight.shape, dtype=self.dtype)
if not self.no_bias:
bias = fluid.data("bias", self.bias.shape, dtype=self.dtype)
y = F.conv_transpose3d(
y = F.conv3d_transpose(
x,
weight,
None if self.no_bias else bias,
......@@ -138,7 +138,7 @@ class TestFunctionalConv3DTranspose(TestCase):
x = dg.to_variable(self.input)
weight = dg.to_variable(self.weight)
bias = None if self.no_bias else dg.to_variable(self.bias)
y = F.conv_transpose3d(
y = F.conv3d_transpose(
x,
weight,
bias,
......@@ -222,7 +222,7 @@ class TestFunctionalConv3DTransposeError(TestCase):
"weight", self.weight_shape, dtype=self.dtype)
if not self.no_bias:
bias = fluid.data("bias", self.bias_shape, dtype=self.dtype)
y = F.conv_transpose3d(
y = F.conv3d_transpose(
x,
weight,
None if self.no_bias else bias,
......
python/paddle/fluid/tests/unittests/test_pool1d_api.py
浏览文件 @ 99408718
......@@ -148,11 +148,7 @@ class TestPool1D_API(unittest.TestCase):
input_np = np.random.random([2, 3, 32]).astype("float32")
input = fluid.dygraph.to_variable(input_np)
result = F.avg_pool1d(
input,
kernel_size=2,
stride=2,
padding=[1],
count_include_pad=True)
input, kernel_size=2, stride=2, padding=[1], exclusive=True)
result_np = avg_pool1D_forward_naive(
input_np, ksize=[2], strides=[2], paddings=[1], exclusive=False)
......@@ -160,7 +156,8 @@ class TestPool1D_API(unittest.TestCase):
self.assertTrue(np.allclose(result.numpy(), result_np))
avg_pool1d_dg = paddle.nn.AvgPool1D(
kernel_size=2, stride=None, padding=1, count_include_pad=True)
kernel_size=2, stride=None, padding=1, exclusive=True)
result = avg_pool1d_dg(input)
self.assertTrue(np.allclose(result.numpy(), result_np))
......@@ -200,7 +197,7 @@ class TestPool1D_API(unittest.TestCase):
input_np = np.random.random([2, 3, 32]).astype("float32")
input = fluid.dygraph.to_variable(input_np)
result, index = F.max_pool1d(
input, kernel_size=2, stride=2, padding=0, return_indices=True)
input, kernel_size=2, stride=2, padding=0, return_mask=True)
result_np = max_pool1D_forward_naive(
input_np, ksize=[2], strides=[2], paddings=[0])
......
python/paddle/fluid/tests/unittests/test_pool2d_api.py
浏览文件 @ 99408718
......@@ -134,7 +134,7 @@ class TestPool2D_API(unittest.TestCase):
input_np = np.random.random([2, 3, 32, 32]).astype("float32")
input = fluid.dygraph.to_variable(input_np)
result = max_pool2d(
input, kernel_size=2, stride=2, padding=0, return_indices=False)
input, kernel_size=2, stride=2, padding=0, return_mask=False)
result_np = pool2D_forward_naive(
input_np,
......@@ -159,7 +159,7 @@ class TestPool2D_API(unittest.TestCase):
kernel_size=2,
stride=2,
padding=0,
return_indices=False,
return_mask=False,
data_format="NHWC")
result_np = pool2D_forward_naive(
......@@ -222,7 +222,7 @@ class TestPool2D_API(unittest.TestCase):
kernel_size=2,
stride=None,
padding="SAME",
return_indices=True)
return_mask=True)
result_np = pool2D_forward_naive(
input_np,
......@@ -269,7 +269,7 @@ class TestPool2D_API(unittest.TestCase):
kernel_size=2,
stride=2,
padding=padding,
return_indices=False)
return_mask=False)
result_np = pool2D_forward_naive(
input_np,
......@@ -490,7 +490,7 @@ class TestPool2DError_API(unittest.TestCase):
padding=0,
ceil_mode=False,
data_format='NHWC',
return_indices=True)
return_mask=True)
self.assertRaises(ValueError, run9)
......
python/paddle/fluid/tests/unittests/test_pool3d_api.py
浏览文件 @ 99408718
......@@ -83,7 +83,7 @@ class TestPool3D_API(unittest.TestCase):
stride=2,
padding=1,
ceil_mode=False,
count_include_pad=True)
exclusive=True)
result_np = avg_pool3D_forward_naive(
input_np,
......@@ -100,7 +100,7 @@ class TestPool3D_API(unittest.TestCase):
stride=None,
padding=1,
ceil_mode=False,
count_include_pad=True)
exclusive=True)
result = avg_pool3d_dg(input)
self.assertTrue(np.allclose(result.numpy(), result_np))
......@@ -175,7 +175,7 @@ class TestPool3D_API(unittest.TestCase):
stride=2,
padding=0,
data_format="NDHWC",
return_indices=False)
return_mask=False)
result_np = pool3D_forward_naive(
input_np,
......@@ -239,7 +239,7 @@ class TestPool3D_API(unittest.TestCase):
kernel_size=2,
stride=None,
padding="SAME",
return_indices=True)
return_mask=True)
result_np = pool3D_forward_naive(
input_np,
......@@ -467,7 +467,7 @@ class TestPool3DError_API(unittest.TestCase):
stride=2,
padding=0,
data_format='NDHWC',
return_indices=True)
return_mask=True)
self.assertRaises(ValueError, run10)
......
python/paddle/fluid/tests/unittests/test_reduce_op.py
浏览文件 @ 99408718
......@@ -767,5 +767,117 @@ class API_TestSumOp(unittest.TestCase):
self.assertTrue((out3 == np.sum(np_x, axis=(0, 1, 2))).all())
class TestAllAPI(unittest.TestCase):
def setUp(self):
np.random.seed(123)
paddle.enable_static()
self.places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda():
self.places.append(fluid.CUDAPlace(0))
def check_static_result(self, place):
with fluid.program_guard(fluid.Program(), fluid.Program()):
input = fluid.data(name="input", shape=[4, 4], dtype="bool")
result = paddle.all(x=input)
input_np = np.random.randint(0, 2, [4, 4]).astype("bool")
exe = fluid.Executor(place)
fetches = exe.run(fluid.default_main_program(),
feed={"input": input_np},
fetch_list=[result])
self.assertTrue(np.allclose(fetches[0], np.all(input_np)))
def test_static(self):
for place in self.places:
self.check_static_result(place=place)
def test_dygraph(self):
paddle.disable_static()
for place in self.places:
with fluid.dygraph.guard(place):
np_x = np.random.randint(0, 2, (12, 10)).astype(np.bool)
x = fluid.layers.assign(np_x)
x = fluid.layers.cast(x, 'bool')
out1 = paddle.all(x)
np_out1 = out1.numpy()
expect_res1 = np.all(np_x)
self.assertTrue((np_out1 == expect_res1).all())
out2 = paddle.all(x, axis=0)
np_out2 = out2.numpy()
expect_res2 = np.all(np_x, axis=0)
self.assertTrue((np_out2 == expect_res2).all())
out3 = paddle.all(x, axis=-1)
np_out3 = out3.numpy()
expect_res3 = np.all(np_x, axis=-1)
self.assertTrue((np_out3 == expect_res3).all())
out4 = paddle.all(x, axis=1, keepdim=True)
np_out4 = out4.numpy()
expect_res4 = np.all(np_x, axis=1, keepdims=True)
self.assertTrue((np_out4 == expect_res4).all())
paddle.enable_static()
class TestAnyAPI(unittest.TestCase):
def setUp(self):
np.random.seed(123)
paddle.enable_static()
self.places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda():
self.places.append(fluid.CUDAPlace(0))
def check_static_result(self, place):
with fluid.program_guard(fluid.Program(), fluid.Program()):
input = fluid.data(name="input", shape=[4, 4], dtype="bool")
result = paddle.any(x=input)
input_np = np.random.randint(0, 2, [4, 4]).astype("bool")
exe = fluid.Executor(place)
fetches = exe.run(fluid.default_main_program(),
feed={"input": input_np},
fetch_list=[result])
self.assertTrue(np.allclose(fetches[0], np.any(input_np)))
def test_static(self):
for place in self.places:
self.check_static_result(place=place)
def test_dygraph(self):
paddle.disable_static()
for place in self.places:
with fluid.dygraph.guard(place):
np_x = np.random.randint(0, 2, (12, 10)).astype(np.bool)
x = fluid.layers.assign(np_x)
x = fluid.layers.cast(x, 'bool')
out1 = paddle.any(x)
np_out1 = out1.numpy()
expect_res1 = np.any(np_x)
self.assertTrue((np_out1 == expect_res1).all())
out2 = paddle.any(x, axis=0)
np_out2 = out2.numpy()
expect_res2 = np.any(np_x, axis=0)
self.assertTrue((np_out2 == expect_res2).all())
out3 = paddle.any(x, axis=-1)
np_out3 = out3.numpy()
expect_res3 = np.any(np_x, axis=-1)
self.assertTrue((np_out3 == expect_res3).all())
out4 = paddle.any(x, axis=1, keepdim=True)
np_out4 = out4.numpy()
expect_res4 = np.any(np_x, axis=1, keepdims=True)
self.assertTrue((np_out4 == expect_res4).all())
paddle.enable_static()
if __name__ == '__main__':
import paddle
paddle.enable_static()
unittest.main()
python/paddle/nn/functional/__init__.py
浏览文件 @ 99408718
......@@ -73,12 +73,12 @@ from .common import interpolate #DEFINE_ALIAS
from .common import upsample #DEFINE_ALIAS
from .common import bilinear #DEFINE_ALIAS
from .conv import conv1d #DEFINE_ALIAS
from .conv import conv_transpose1d #DEFINE_ALIAS
from .conv import conv1d_transpose #DEFINE_ALIAS
from .common import linear #DEFINE_ALIAS
from .conv import conv2d #DEFINE_ALIAS
from .conv import conv_transpose2d #DEFINE_ALIAS
from .conv import conv2d_transpose #DEFINE_ALIAS
from .conv import conv3d #DEFINE_ALIAS
from .conv import conv_transpose3d #DEFINE_ALIAS
from .conv import conv3d_transpose #DEFINE_ALIAS
# from .extension import add_position_encoding #DEFINE_ALIAS
# from .extension import autoincreased_step_counter #DEFINE_ALIAS
# from .extension import continuous_value_model #DEFINE_ALIAS
......
python/paddle/nn/functional/conv.py
浏览文件 @ 99408718
......@@ -15,11 +15,11 @@ from __future__ import print_function
__all__ = [
'conv1d',
'conv_transpose1d',
'conv1d_transpose',
'conv2d',
'conv_transpose2d',
'conv2d_transpose',
'conv3d',
'conv_transpose3d',
'conv3d_transpose',
]
import numpy as np
......@@ -541,7 +541,7 @@ def conv2d(x,
return out
def conv_transpose1d(x,
def conv1d_transpose(x,
weight,
bias=None,
stride=1,
......@@ -682,7 +682,7 @@ def conv_transpose1d(x,
[[4, 2]]]).astype(np.float32)
x_var = paddle.to_tensor(x)
w_var = paddle.to_tensor(w)
y_var = F.conv_transpose1d(x_var, w_var)
y_var = F.conv1d_transpose(x_var, w_var)
y_np = y_var.numpy()
print y_np
......@@ -802,7 +802,7 @@ def conv_transpose1d(x,
return out
def conv_transpose2d(x,
def conv2d_transpose(x,
weight,
bias=None,
stride=1,
......@@ -920,7 +920,7 @@ def conv_transpose2d(x,
None by default.
Returns:
A Tensor representing the conv_transpose2d, whose
A Tensor representing the conv2d_transpose, whose
data type is the same with input and shape is (num_batches, channels, out_h,
out_w) or (num_batches, out_h, out_w, channels). The tensor variable storing
transposed convolution result.
......@@ -946,7 +946,7 @@ def conv_transpose2d(x,
x_var = paddle.randn((2, 3, 8, 8), dtype='float32')
w_var = paddle.randn((3, 6, 3, 3), dtype='float32')
y_var = F.conv_transpose2d(x_var, w_var)
y_var = F.conv2d_transpose(x_var, w_var)
y_np = y_var.numpy()
print(y_np.shape)
......@@ -1242,7 +1242,7 @@ def conv3d(x,
return out
def conv_transpose3d(x,
def conv3d_transpose(x,
weight,
bias=None,
stride=1,
......@@ -1364,7 +1364,7 @@ def conv_transpose3d(x,
None by default.
Returns:
A Tensor representing the conv_transpose3d, whose data
A Tensor representing the conv3d_transpose, whose data
type is the same with input and shape is (num_batches, channels, out_d, out_h,
out_w) or (num_batches, out_d, out_h, out_w, channels). If act is None, the tensor
variable storing the transposed convolution result, and if act is not None, the tensor
......@@ -1391,7 +1391,7 @@ def conv_transpose3d(x,
x_var = paddle.randn((2, 3, 8, 8, 8), dtype='float32')
w_var = paddle.randn((3, 6, 3, 3, 3), dtype='float32')
y_var = F.conv_transpose3d(x_var, w_var)
y_var = F.conv3d_transpose(x_var, w_var)
y_np = y_var.numpy()
print(y_np.shape)
......
python/paddle/nn/layer/conv.py
浏览文件 @ 99408718
......@@ -427,7 +427,7 @@ class Conv1DTranspose(_ConvNd):
data_format=data_format)
def forward(self, x, output_size=None):
out = F.conv_transpose1d(
out = F.conv1d_transpose(
x,
self.weight,
bias=self.bias,
......@@ -748,7 +748,7 @@ class Conv2DTranspose(_ConvNd):
else:
output_padding = 0
out = F.conv_transpose2d(
out = F.conv2d_transpose(
x,
self.weight,
bias=self.bias,
......@@ -954,16 +954,16 @@ class Conv3DTranspose(_ConvNd):
**Note**:
The conv_transpose3d can be seen as the backward of the conv3d. For conv3d,
The conv3d_transpose can be seen as the backward of the conv3d. For conv3d,
when stride > 1, conv3d maps multiple input shape to the same output shape,
so for conv_transpose3d, when stride > 1, input shape maps multiple output shape.
so for conv3d_transpose, when stride > 1, input shape maps multiple output shape.
If output_size is None, :math:`H_{out} = H^\prime_{out}, :math:`H_{out} = \
H^\prime_{out}, W_{out} = W^\prime_{out}`; else, the :math:`D_{out}` of the output
size must between :math:`D^\prime_{out}` and :math:`D^\prime_{out} + strides[0]`,
the :math:`H_{out}` of the output size must between :math:`H^\prime_{out}`
and :math:`H^\prime_{out} + strides[1]`, and the :math:`W_{out}` of the output size must
between :math:`W^\prime_{out}` and :math:`W^\prime_{out} + strides[2]`,
conv_transpose3d can compute the kernel size automatically.
conv3d_transpose can compute the kernel size automatically.
Parameters:
in_channels(int): The number of channels in the input image.
......@@ -1086,7 +1086,7 @@ class Conv3DTranspose(_ConvNd):
else:
output_padding = 0
out = F.conv_transpose3d(
out = F.conv3d_transpose(
x,
self.weight,
bias=self.bias,
......
python/paddle/nn/layer/norm.py
浏览文件 @ 99408718
......@@ -73,7 +73,6 @@ class _InstanceNormBase(layers.Layer):
momentum=0.9,
weight_attr=None,
bias_attr=None,
track_running_stats=False,
data_format="NCHW",
name=None):
super(_InstanceNormBase, self).__init__()
......@@ -135,9 +134,6 @@ class InstanceNorm1D(_InstanceNormBase):
epsilon(float, optional): A value added to the denominator for
numerical stability. Default is 1e-5.
momentum(float, optional): The value used for the moving_mean and moving_var computation. Default: 0.9.
track_running_stats(bool, optional): Whether to use global mean and
variance. In train mode, when setting track_running_stats True, the global mean
and variance are also used during train period. Default: False.
weight_attr(ParamAttr|bool, optional): The parameter attribute for Parameter `scale`
of instance_norm. If it is set to None or one attribute of ParamAttr, instance_norm
will create ParamAttr as weight_attr, the name of scale can be set in ParamAttr.
......@@ -159,9 +155,6 @@ class InstanceNorm1D(_InstanceNormBase):
Returns:
None.
**Note**:
Momentum and track_running_stats is not effective. The next version will fix the problem .
Examples:
......@@ -214,9 +207,6 @@ class InstanceNorm2D(_InstanceNormBase):
epsilon(float, optional): A value added to the denominator for
numerical stability. Default is 1e-5.
momentum(float, optional): The value used for the moving_mean and moving_var computation. Default: 0.9.
track_running_stats(bool, optional): Whether to use global mean and
variance. In train mode, when setting track_running_stats True, the global mean
and variance are also used during train period. Default: False.
weight_attr(ParamAttr|bool, optional): The parameter attribute for Parameter `scale`
of instance_norm. If it is set to None or one attribute of ParamAttr, instance_norm
will create ParamAttr as weight_attr, the name of scale can be set in ParamAttr.
......@@ -237,8 +227,6 @@ class InstanceNorm2D(_InstanceNormBase):
Returns:
None.
**Note**:
Momentum and track_running_stats is not effective. The next version will fix the problem .
Examples:
......@@ -290,9 +278,6 @@ class InstanceNorm3D(_InstanceNormBase):
epsilon(float, optional): A value added to the denominator for
numerical stability. Default is 1e-5.
momentum(float, optional): The value used for the moving_mean and moving_var computation. Default: 0.9.
track_running_stats(bool, optional): Whether to use global mean and
variance. In train mode, when setting track_running_stats True, the global mean
and variance are also used during train period. Default: False.
weight_attr(ParamAttr|bool, optional): The parameter attribute for Parameter `scale`
of instance_norm. If it is set to None or one attribute of ParamAttr, instance_norm
will create ParamAttr as weight_attr, the name of scale can be set in ParamAttr.
......@@ -313,8 +298,6 @@ class InstanceNorm3D(_InstanceNormBase):
Returns:
None.
**Note**:
Momentum and track_running_stats is not effective. The next version will fix the problem .
Examples:
......@@ -570,7 +553,6 @@ class _BatchNormBase(layers.Layer):
weight_attr=None,
bias_attr=None,
data_format='NCHW',
track_running_stats=True,
name=None):
super(_BatchNormBase, self).__init__()
self._num_features = num_features
......@@ -636,7 +618,6 @@ class _BatchNormBase(layers.Layer):
self._momentum = momentum
self._epsilon = epsilon
self._fuse_with_relu = False
self._track_running_stats = track_running_stats
self._name = name
def _check_input_dim(self, input):
......@@ -651,11 +632,7 @@ class _BatchNormBase(layers.Layer):
self._check_input_dim(input)
if not self.training and not self._track_running_stats:
raise ValueError(
'When inference, expected track_running_stats is True.')
if self.training and not self._track_running_stats:
if self.training:
warnings.warn(
"When training, we now always track global mean and variance.")
......@@ -720,9 +697,6 @@ class BatchNorm1D(_BatchNormBase):
will create ParamAttr as bias_attr. If it is set to Fasle, the weight is not learnable.
If the Initializer of the bias_attr is not set, the bias is initialized zero. Default: None.
data_format(str, optional): Specify the input data format, may be "NC", "NCL" or "NLC". Defalut "NCL".
track_running_stats(bool, optional): Whether to use global mean and variance. In train period,
True will track global mean and variance used for inference. When inference, track_running_stats must be
True. Default: True.
name(str, optional): Name for the BatchNorm, default is None. For more information, please refer to :ref:`api_guide_Name`..
Shape:
......@@ -733,9 +707,6 @@ class BatchNorm1D(_BatchNormBase):
Returns:
None.
**Note**:
Now track_running_stats is actucal always true. The next version will fix the problem .
Examples:
.. code-block:: python
......@@ -817,9 +788,6 @@ class BatchNorm2D(_BatchNormBase):
will create ParamAttr as bias_attr. If it is set to Fasle, the weight is not learnable.
If the Initializer of the bias_attr is not set, the bias is initialized zero. Default: None.
data_format(str, optional): Specify the input data format, the data format can be "NCHW" or "NHWC". Default: NCHW.
track_running_stats(bool, optional): Whether to use global mean and variance. In train period,
True will track global mean and variance used for inference. When inference, track_running_stats must be
True. Default: True.
name(str, optional): Name for the BatchNorm, default is None. For more information, please refer to :ref:`api_guide_Name`..
Shape:
......@@ -830,9 +798,6 @@ class BatchNorm2D(_BatchNormBase):
Returns:
None
**Note**:
Now track_running_stats is actucal always true. The next version will fix the problem .
Examples:
.. code-block:: python
......@@ -912,9 +877,6 @@ class BatchNorm3D(_BatchNormBase):
will create ParamAttr as bias_attr. If it is set to Fasle, the weight is not learnable.
If the Initializer of the bias_attr is not set, the bias is initialized zero. Default: None.
data_format(str, optional): Specify the input data format, the data format can be "NCDHW" or "NDHWC. Default: NCDHW.
track_running_stats(bool, optional): Whether to use global mean and variance. In train period,
True will track global mean and variance used for inference. When inference, track_running_stats must be
True. Default: True.
name(str, optional): Name for the BatchNorm, default is None. For more information, please refer to :ref:`api_guide_Name`..
Shape:
......@@ -925,9 +887,6 @@ class BatchNorm3D(_BatchNormBase):
Returns:
None
**Note**:
Now track_running_stats is actucal always true. The next version will fix the problem .
Examples:
.. code-block:: python
......@@ -1024,8 +983,6 @@ class SyncBatchNorm(_BatchNormBase):
will create ParamAttr as bias_attr. If the Initializer of the bias_attr
is not set, the bias is initialized zero. If it is set to False, this layer will not
have trainable bias parameter. Default: None.
track_running_stats(bool, optional): Whether to compute global stats, which including running mean and
running variance. Default: True.
Shapes:
input: Tensor that the dimension from 2 to 5.
......@@ -1055,11 +1012,10 @@ class SyncBatchNorm(_BatchNormBase):
weight_attr=None,
bias_attr=None,
data_format='NCHW',
track_running_stats=True,
name=None):
super(SyncBatchNorm,
self).__init__(num_features, momentum, epsilon, weight_attr,
bias_attr, data_format, track_running_stats, name)
bias_attr, data_format, name)
def forward(self, x):
# create output
......@@ -1147,10 +1103,10 @@ class SyncBatchNorm(_BatchNormBase):
"""
layer_output = layer
if isinstance(layer, _BatchNormBase):
layer_output = SyncBatchNorm(
layer._num_features, layer._momentum, layer._epsilon,
layer._weight_attr, layer._bias_attr, layer._data_format,
layer._track_running_stats, layer._name)
layer_output = SyncBatchNorm(layer._num_features, layer._momentum,
layer._epsilon, layer._weight_attr,
layer._bias_attr, layer._data_format,
layer._name)
if layer._weight_attr != False and layer._bias_attr != False:
with no_grad():
......
python/paddle/nn/layer/pooling.py
浏览文件 @ 99408718
......@@ -35,7 +35,7 @@ __all__ = [
class AvgPool1D(layers.Layer):
"""
This operation applies a 1D average pooling over an input signal composed
of several input planes, based on the input, output_size, return_indices parameters.
of several input planes, based on the input, output_size, return_mask parameters.
Input(X) and output(Out) are in NCL format, where N is batch
size, C is the number of channels, L is the length of the feature.
The output tensor shape will be [N, C, output_size].
......@@ -61,7 +61,7 @@ class AvgPool1D(layers.Layer):
4. A list[int] or tuple(int) whose length is 2. It has the form [pad_before, pad_after].
5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
The default value is 0.
count_include_pad (bool): Whether to exclude padding points in average pooling
exclusive (bool): Whether to exclude padding points in average pooling
mode, default is `True`.
ceil_mode (bool): ${ceil_mode_comment}Whether to use the ceil function to calculate output height and width.
If it is set to False, the floor function will be used. The default value is False.
......@@ -103,7 +103,7 @@ class AvgPool1D(layers.Layer):
kernel_size,
stride=None,
padding=0,
count_include_pad=True,
exclusive=True,
ceil_mode=False,
name=None):
super(AvgPool1D, self).__init__()
......@@ -111,12 +111,12 @@ class AvgPool1D(layers.Layer):
self.stride = stride
self.padding = padding
self.ceil_mode = ceil_mode
self.count_include_pad = count_include_pad
self.exclusive = exclusive
self.name = name
def forward(self, x):
out = F.avg_pool1d(x, self.kernel_size, self.stride, self.padding,
self.count_include_pad, self.ceil_mode, self.name)
self.exclusive, self.ceil_mode, self.name)
return out
......@@ -156,7 +156,7 @@ class AvgPool2D(layers.Layer):
5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
The default value is 0.
ceil_mode (bool): when True, will use `ceil` instead of `floor` to compute the output shape
count_include_pad (bool): Whether to exclude padding points in average pooling
exclusive (bool): Whether to exclude padding points in average pooling
mode, default is `true`.
divisor_override (float): if specified, it will be used as divisor, otherwise kernel_size will be used. Default None.
data_format (string): The data format of the input and output data. An optional string from: `"NCHW"`, `"NDHW"`.
......@@ -197,7 +197,7 @@ class AvgPool2D(layers.Layer):
stride=None,
padding=0,
ceil_mode=False,
count_include_pad=True,
exclusive=True,
divisor_override=None,
data_format="NCHW",
name=None):
......@@ -206,7 +206,7 @@ class AvgPool2D(layers.Layer):
self.stride = stride
self.padding = padding
self.ceil_mode = ceil_mode
self.count_include_pad = count_include_pad
self.exclusive = exclusive
self.divisor = divisor_override
self.data_format = data_format
self.name = name
......@@ -218,7 +218,7 @@ class AvgPool2D(layers.Layer):
stride=self.stride,
padding=self.padding,
ceil_mode=self.ceil_mode,
count_include_pad=self.count_include_pad,
exclusive=self.exclusive,
divisor_override=self.divisor,
data_format=self.data_format,
name=self.name)
......@@ -247,7 +247,7 @@ class AvgPool3D(layers.Layer):
5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
The default value is 0.
ceil_mode (bool): ${ceil_mode_comment}
count_include_pad (bool): Whether to exclude padding points in average pooling
exclusive (bool): Whether to exclude padding points in average pooling
mode, default is True.
divisor_override (int|float) if specified, it will be used as divisor, otherwise kernel_size will be used. Default None.
data_format (string): The data format of the input and output data. An optional string from: `"NCDHW"`, `"NDHWC"`.
......@@ -289,7 +289,7 @@ class AvgPool3D(layers.Layer):
stride,
padding=0,
ceil_mode=False,
count_include_pad=True,
exclusive=True,
divisor_override=None,
data_format="NCDHW",
name=None):
......@@ -298,7 +298,7 @@ class AvgPool3D(layers.Layer):
self.stride = stride
self.padding = padding
self.ceil_mode = ceil_mode
self.count_include_pad = count_include_pad
self.exclusive = exclusive
self.divisor = divisor_override
self.data_format = data_format
self.name = name
......@@ -310,7 +310,7 @@ class AvgPool3D(layers.Layer):
stride=self.stride,
padding=self.padding,
ceil_mode=self.ceil_mode,
count_include_pad=self.count_include_pad,
exclusive=self.exclusive,
divisor_override=self.divisor,
data_format=self.data_format,
name=self.name)
......@@ -319,7 +319,7 @@ class AvgPool3D(layers.Layer):
class MaxPool1D(layers.Layer):
"""
Applies a 1D max pooling over an input signal composed of several input planes based
on the input, output_size, return_indices parameters.
on the input, output_size, return_mask parameters.
Input(X) and output(Out) are in NCL format, where N is batch
size, C is the number of channels, L is the length of the feature.
......@@ -343,7 +343,7 @@ class MaxPool1D(layers.Layer):
4. A list[int] or tuple(int) whose length is 2. It has the form [pad_before, pad_after].
5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
The default value is 0.
return_indices (bool): Whether return the max indices along with the outputs. default is `False`.
return_mask (bool): Whether return the max indices along with the outputs. default is `False`.
ceil_mode (bool): Whether to use the ceil function to calculate output height and width. False is the default.
If it is set to False, the floor function will be used. Default False.
name(str, optional): For detailed information, please refer
......@@ -377,7 +377,7 @@ class MaxPool1D(layers.Layer):
pool_out = MaxPool1D(data)
# pool_out shape: [1, 3, 16]
MaxPool1D = nn.MaxPool1D(kernel_size=2, stride=2, padding=0, return_indices=True)
MaxPool1D = nn.MaxPool1D(kernel_size=2, stride=2, padding=0, return_mask=True)
pool_out, indices = MaxPool1D(data)
# pool_out shape: [1, 3, 16], indices shape: [1, 3, 16]
......@@ -387,7 +387,7 @@ class MaxPool1D(layers.Layer):
kernel_size,
stride=None,
padding=0,
return_indices=False,
return_mask=False,
ceil_mode=False,
name=None):
super(MaxPool1D, self).__init__()
......@@ -395,12 +395,12 @@ class MaxPool1D(layers.Layer):
self.stride = stride
self.padding = padding
self.ceil_mode = ceil_mode
self.return_indices = return_indices
self.return_mask = return_mask
self.name = name
def forward(self, input):
out = F.max_pool1d(input, self.kernel_size, self.stride, self.padding,
self.return_indices, self.ceil_mode, self.name)
self.return_mask, self.ceil_mode, self.name)
return out
......@@ -440,7 +440,7 @@ class MaxPool2D(layers.Layer):
5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
The default value is 0.
ceil_mode (bool): when True, will use `ceil` instead of `floor` to compute the output shape
return_indices (bool): Whether to return the max indices along with the outputs.
return_mask (bool): Whether to return the max indices along with the outputs.
data_format (string): The data format of the input and output data. An optional string from: `"NCHW"`, `"NDHW"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`.
......@@ -473,8 +473,8 @@ class MaxPool2D(layers.Layer):
output = MaxPool2D(input)
# output.shape [1, 3, 16, 16]
# for return_indices=True
MaxPool2D = nn.MaxPool2D(kernel_size=2,stride=2, padding=0, return_indices=True)
# for return_mask=True
MaxPool2D = nn.MaxPool2D(kernel_size=2, stride=2, padding=0, return_mask=True)
output, max_indices = MaxPool2D(input)
# output.shape [1, 3, 16, 16], max_indices.shape [1, 3, 16, 16],
"""
......@@ -483,7 +483,7 @@ class MaxPool2D(layers.Layer):
kernel_size,
stride=None,
padding=0,
return_indices=False,
return_mask=False,
ceil_mode=False,
data_format="NCHW",
name=None):
......@@ -491,7 +491,7 @@ class MaxPool2D(layers.Layer):
self.ksize = kernel_size
self.stride = stride
self.padding = padding
self.return_indices = return_indices
self.return_mask = return_mask
self.ceil_mode = ceil_mode
self.data_format = data_format
self.name = name
......@@ -502,7 +502,7 @@ class MaxPool2D(layers.Layer):
kernel_size=self.ksize,
stride=self.stride,
padding=self.padding,
return_indices=self.return_indices,
return_mask=self.return_mask,
data_format=self.data_format,
name=self.name)
......@@ -530,7 +530,7 @@ class MaxPool3D(layers.Layer):
5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
The default value is 0.
ceil_mode (bool): ${ceil_mode_comment}
return_indices (bool): Whether to return the max indices along with the outputs.
return_mask (bool): Whether to return the max indices along with the outputs.
data_format (string): The data format of the input and output data. An optional string from: `"NCDHW"`, `"NDHWC"`.
The default is `"NCDHW"`. When it is `"NCDHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_depth, input_height, input_width]`.
......@@ -564,8 +564,8 @@ class MaxPool3D(layers.Layer):
output = MaxPool3D(input)
# output.shape [1, 2, 3, 16, 16]
# for return_indices=True
MaxPool3D = nn.MaxPool3D(kernel_size=2,stride=2, padding=0, return_indices=True)
# for return_mask=True
MaxPool3D = nn.MaxPool3D(kernel_size=2, stride=2, padding=0, return_mask=True)
output, max_indices = MaxPool3D(input)
# output.shape [1, 2, 3, 16, 16], max_indices.shape [1, 2, 3, 16, 16],
"""
......@@ -574,7 +574,7 @@ class MaxPool3D(layers.Layer):
kernel_size,
stride,
padding,
return_indices=False,
return_mask=False,
ceil_mode=False,
data_format="NCDHW",
name=None):
......@@ -582,7 +582,7 @@ class MaxPool3D(layers.Layer):
self.ksize = kernel_size
self.stride = stride
self.padding = padding
self.return_indices = return_indices
self.return_mask = return_mask
self.ceil_mode = ceil_mode
self.data_format = data_format
self.name = name
......@@ -593,7 +593,7 @@ class MaxPool3D(layers.Layer):
kernel_size=self.ksize,
stride=self.stride,
padding=self.padding,
return_indices=self.return_indices,
return_mask=self.return_mask,
data_format=self.data_format,
name=self.name)
......@@ -602,7 +602,7 @@ class AdaptiveAvgPool1D(layers.Layer):
"""
This operation applies a 1D adaptive average pooling over an input signal composed
of several input planes, based on the input, output_size, return_indices parameters.
of several input planes, based on the input, output_size, return_mask parameters.
Input(X) and output(Out) are in NCL format, where N is batch
size, C is the number of channels, L is the length of the feature.
The output tensor shape will be [N, C, output_size].
......@@ -841,7 +841,7 @@ class AdaptiveMaxPool1D(layers.Layer):
"""
This operation applies a 1D adaptive max pooling over an input signal composed
of several input planes, based on the input, output_size, return_indices parameters.
of several input planes, based on the input, output_size, return_mask parameters.
Input(X) and output(Out) are in NCL format, where N is batch
size, C is the number of channels, L is the length of the feature.
The output tensor shape will be [N, C, output_size].
......@@ -859,7 +859,7 @@ class AdaptiveMaxPool1D(layers.Layer):
Args:
output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
it must contain one int.
return_indices (bool): If true, the index of max pooling point will be returned along
return_mask (bool): If true, the index of max pooling point will be returned along
with outputs. It cannot be set in average pooling type. Default False.
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
......@@ -898,22 +898,22 @@ class AdaptiveMaxPool1D(layers.Layer):
pool_out = AdaptiveMaxPool1D(data)
# pool_out shape: [1, 3, 16]
# for return_indices = true
AdaptiveMaxPool1D = nn.AdaptiveMaxPool1D(output_size=16, return_indices=True)
# for return_mask = true
AdaptiveMaxPool1D = nn.AdaptiveMaxPool1D(output_size=16, return_mask=True)
pool_out, indices = AdaptiveMaxPool1D(data)
# pool_out shape: [1, 3, 16], indices shape: [1, 3, 16]
"""
def __init__(self, output_size, return_indices=False, name=None):
def __init__(self, output_size, return_mask=False, name=None):
super(AdaptiveMaxPool1D, self).__init__()
self.output_size = output_size
self.return_indices = return_indices
self.return_mask = return_mask
self.name = name
def forward(self, input):
return F.adaptive_max_pool1d(input, self.output_size,
self.return_indices, self.name)
return F.adaptive_max_pool1d(input, self.output_size, self.return_mask,
self.name)
class AdaptiveMaxPool2D(layers.Layer):
......@@ -932,7 +932,7 @@ class AdaptiveMaxPool2D(layers.Layer):
Output(i ,j) &= max(Input[hstart:hend, wstart:wend])
Parameters:
output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list, it must contain two element, (H, W). H and W can be either a int, or None which means the size will be the same as that of the input.
return_indices (bool): If true, the index of max pooling point will be returned along with outputs. It cannot be set in average pooling type. Default False.
return_mask (bool): If true, the index of max pooling point will be returned along with outputs. It cannot be set in average pooling type. Default False.
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
......@@ -965,21 +965,21 @@ class AdaptiveMaxPool2D(layers.Layer):
paddle.disable_static()
input_data = np.random.rand(2, 3, 32, 32)
x = paddle.to_tensor(input_data)
adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=3, return_indices=True)
adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=3, return_mask=True)
pool_out, indices = adaptive_max_pool(x = x)
"""
def __init__(self, output_size, return_indices=False, name=None):
def __init__(self, output_size, return_mask=False, name=None):
super(AdaptiveMaxPool2D, self).__init__()
self._output_size = output_size
self._return_indices = return_indices
self._return_mask = return_mask
self._name = name
def forward(self, x):
return F.adaptive_max_pool2d(
x,
output_size=self._output_size,
return_indices=self._return_indices,
return_mask=self._return_mask,
name=self._name)
......@@ -1002,7 +1002,7 @@ class AdaptiveMaxPool3D(layers.Layer):
Parameters:
output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list, it must contain three elements, (D, H, W). D, H and W can be either a int, or None which means the size will be the same as that of the input.
return_indices (bool): If true, the index of max pooling point will be returned along with outputs. Default False.
return_mask (bool): If true, the index of max pooling point will be returned along with outputs. Default False.
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
......@@ -1040,21 +1040,21 @@ class AdaptiveMaxPool3D(layers.Layer):
pool = paddle.nn.AdaptiveMaxPool3D(output_size=4)
out = pool(x)
# out shape: [2, 3, 4, 4, 4]
pool = paddle.nn.AdaptiveMaxPool3D(output_size=3, return_indices=True)
pool = paddle.nn.AdaptiveMaxPool3D(output_size=3, return_mask=True)
out, indices = pool(x)
# out shape: [2, 3, 4, 4, 4], indices shape: [2, 3, 4, 4, 4]
"""
def __init__(self, output_size, return_indices=False, name=None):
def __init__(self, output_size, return_mask=False, name=None):
super(AdaptiveMaxPool3D, self).__init__()
self._output_size = output_size
self._return_indices = return_indices
self._return_mask = return_mask
self._name = name
def forward(self, x):
return F.adaptive_max_pool3d(
x,
output_size=self._output_size,
return_indices=self._return_indices,
return_mask=self._return_mask,
name=self._name)
python/paddle/tensor/__init__.py
浏览文件 @ 99408718
......@@ -66,8 +66,6 @@ from .logic import logical_not #DEFINE_ALIAS
from .logic import logical_or #DEFINE_ALIAS
from .logic import logical_xor #DEFINE_ALIAS
from .logic import not_equal #DEFINE_ALIAS
# from .logic import reduce_all #DEFINE_ALIAS
# from .logic import reduce_any #DEFINE_ALIAS
from .logic import allclose #DEFINE_ALIAS
from .logic import equal_all #DEFINE_ALIAS
# from .logic import isnan #DEFINE_ALIAS
......@@ -164,6 +162,8 @@ from .math import isfinite #DEFINE_ALIAS
from .math import isinf #DEFINE_ALIAS
from .math import isnan #DEFINE_ALIAS
from .math import prod #DEFINE_ALIAS
from .math import all #DEFINE_ALIAS
from .math import any #DEFINE_ALIAS
from .random import multinomial #DEFINE_ALIAS
from .random import standard_normal
from .random import normal
......
python/paddle/tensor/logic.py
浏览文件 @ 99408718
......@@ -29,6 +29,8 @@ from ..fluid.layers import logical_and #DEFINE_ALIAS
from ..fluid.layers import logical_not #DEFINE_ALIAS
from ..fluid.layers import logical_or #DEFINE_ALIAS
from ..fluid.layers import logical_xor #DEFINE_ALIAS
from ..fluid.layers import reduce_all #DEFINE_ALIAS
from ..fluid.layers import reduce_any #DEFINE_ALIAS
__all__ = [
'equal',
......
python/paddle/tensor/math.py
浏览文件 @ 99408718
......@@ -21,7 +21,7 @@ from paddle.common_ops_import import *
from paddle.tensor import cast
import paddle
from ..fluid import layers
from ..fluid.framework import core, _varbase_creator, in_dygraph_mode, Variable
from ..fluid.framework import core, _varbase_creator, in_dygraph_mode, Variable, convert_np_dtype_to_dtype_
from ..fluid.layer_helper import LayerHelper
from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype, convert_dtype
from ..fluid.layers.layer_function_generator import _generate_doc_string_, generate_activation_fn, generate_layer_fn
......@@ -46,6 +46,8 @@ from ..fluid.layers import exp #DEFINE_ALIAS
from ..fluid.layers import floor #DEFINE_ALIAS
from ..fluid.layers import log #DEFINE_ALIAS
from ..fluid.layers import reciprocal #DEFINE_ALIAS
from ..fluid.layers import reduce_all #DEFINE_ALIAS
from ..fluid.layers import reduce_any #DEFINE_ALIAS
# from ..fluid.layers import reduce_max #DEFINE_ALIAS
# from ..fluid.layers import reduce_min #DEFINE_ALIAS
# from ..fluid.layers import reduce_prod #DEFINE_ALIAS
......@@ -1933,3 +1935,201 @@ def increment(x, value=1.0, name=None):
outputs={'Out': [x]},
attrs={'step': float(value)})
return x
def all(x, axis=None, keepdim=False, name=None):
"""
Computes the the ``logical and`` of tensor elements over the given dimension.
Args:
x (Tensor): An N-D Tensor, the input data type should be `bool`.
axis (int|list|tuple, optional): The dimensions along which the ``logical and`` is compute. If
:attr:`None`, and all elements of :attr:`x` and return a
Tensor variable with a single element, otherwise must be in the
range :math:`[-rank(x), rank(x))`. If :math:`axis[i] < 0`,
the dimension to reduce is :math:`rank + axis[i]`.
keepdim (bool, optional): Whether to reserve the reduced dimension in the
output Tensor. The result Tensor will have one fewer dimension
than the :attr:`x` unless :attr:`keepdim` is true, default
value is False.
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:
Tensor: Results the ``logical and`` on the specified axis of input Tensor `x`, it's data type is bool.
Raises:
ValueError: If the data type of `x` is not bool.
TypeError: The type of :attr:`axis` must be int, list or tuple.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import numpy as np
# set as static mode
paddle.disable_static()
# x is a bool Tensor variable with following elements:
# [[True, False]
# [True, True]]
x = layers.assign(np.array([[1, 0], [1, 1]], dtype='int32'))
print(x)
x = layers.cast(x, 'bool')
# out1 should be [False]
out1 = paddle.all(x) # [False]
print(out1)
# out2 should be [True, False]
out2 = paddle.all(x, axis=0) # [True, False]
print(out2)
# keep_dim=False, out3 should be [False, True], out.shape should be (2,)
out3 = paddle.all(x, axis=-1) # [False, True]
print(out3)
# keep_dim=True, out4 should be [[False], [True]], out.shape should be (2,1)
out4 = paddle.all(x, axis=1, keep_dim=True)
out4 = layers.cast(out4, 'int32') # [[False], [True]]
print(out4)
"""
if axis is not None and not isinstance(axis, (list, tuple)):
axis = [axis]
if not axis:
reduce_all_flag = True
else:
if len(axis) == len(x.shape):
reduce_all_flag = True
else:
reduce_all_flag = False
attrs = {
'dim': axis if axis != None and axis != [] and axis != () else [0],
'keep_dim': keepdim,
'reduce_all': reduce_all_flag
}
dtype_flag = False
if in_dygraph_mode():
axis = axis if axis != None and axis != [] else [0]
return core.ops.reduce_all(x, 'dim', axis, 'keep_dim', keepdim,
'reduce_all', reduce_all_flag)
check_variable_and_dtype(x, 'x', ['bool'], 'all')
check_type(axis, 'axis', (int, list, tuple, type(None)), 'all')
helper = LayerHelper('all', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='reduce_all',
inputs={'X': x},
outputs={'Out': out},
attrs=attrs)
return out
def any(x, axis=None, keepdim=False, name=None):
"""
Computes the the ``logical or`` of tensor elements over the given dimension.
Args:
x (Tensor): An N-D Tensor, the input data type should be `bool`.
axis (int|list|tuple, optional): The dimensions along which the ``logical or`` is compute. If
:attr:`None`, and all elements of :attr:`x` and return a
Tensor variable with a single element, otherwise must be in the
range :math:`[-rank(x), rank(x))`. If :math:`axis[i] < 0`,
the dimension to reduce is :math:`rank + axis[i]`.
keepdim (bool, optional): Whether to reserve the reduced dimension in the
output Tensor. The result Tensor will have one fewer dimension
than the :attr:`x` unless :attr:`keepdim` is true, default
value is False.
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:
Tensor: Results the ``logical or`` on the specified axis of input Tensor `x`, it's data type is bool.
Raises:
ValueError: If the data type of `x` is not bool.
TypeError: The type of :attr:`axis` must be int, list or tuple.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import numpy as np
# set as static mode
paddle.disable_static()
# x is a bool Tensor variable with following elements:
# [[True, False]
# [False, False]]
x = layers.assign(np.array([[1, 0], [1, 1]], dtype='int32'))
print(x)
x = layers.cast(x, 'bool')
# out1 should be [True]
out1 = paddle.any(x) # [True]
print(out1)
# out2 should be [True, False]
out2 = paddle.any(x, axis=0) # [True, False]
print(out2)
# keep_dim=False, out3 should be [True, False], out.shape should be (2,)
out3 = paddle.any(x, axis=-1) # [True, False]
print(out3)
# keep_dim=True, result should be [[True], [False]], out.shape should be (2,1)
out4 = paddle.any(x, axis=1, keep_dim=True)
out4 = layers.cast(out4, 'int32') # [[True], [False]]
print(out4)
"""
if axis is not None and not isinstance(axis, (list, tuple)):
axis = [axis]
if not axis:
reduce_all_flag = True
else:
if len(axis) == len(x.shape):
reduce_all_flag = True
else:
reduce_all_flag = False
attrs = {
'dim': axis if axis != None and axis != [] and axis != () else [0],
'keep_dim': keepdim,
'reduce_all': reduce_all_flag
}
dtype_flag = False
if in_dygraph_mode():
axis = axis if axis != None and axis != [] else [0]
return core.ops.reduce_any(x, 'dim', axis, 'keep_dim', keepdim,
'reduce_all', reduce_all_flag)
check_variable_and_dtype(x, 'x', ['bool'], 'any')
check_type(axis, 'axis', (int, list, tuple, type(None)), 'any')
helper = LayerHelper('any', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='reduce_any',
inputs={'X': x},
outputs={'Out': out},
attrs=attrs)
return out
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