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未验证 提交 cb812f40 编写于 作者: Y yunyaoXYY 提交者： GitHub
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[Clean fluid] Clean hash, grid_sampler, log_loss, bilinear_tensor_product. (#48411) 

* Clean fliud hash

* clean fluid grid_sampler

* clean log_loss

* Move bilinear_tensor_product from fluid to static

* Fix unitests when remove log_loss

* Fix bug when move bilinear_tensor_product

* fix test_fleet_nocvm_1.py

* Add bilinear_tensor_product into all list

* Fix code style

* Fix comments in bilinear_tensor_product

* Fix comments in bilinear_tensor_product

* Fix comments
上级 9913da02
隐藏空白更改
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Showing with 211 addition and 566 deletion
python/paddle/fluid/layers/nn.py
浏览文件 @ cb812f40
......@@ -96,10 +96,6 @@ __all__ = [
'clip_by_norm',
'mean',
'mul',
'hash',
'grid_sampler',
'log_loss',
'bilinear_tensor_product',
'merge_selected_rows',
'get_tensor_from_selected_rows',
'unfold',
......@@ -5223,292 +5219,6 @@ def mul(x, y, x_num_col_dims=1, y_num_col_dims=1, name=None):
return out
def hash(input, hash_size, num_hash=1, name=None):
"""
This OP hash the input to an integer less than the hash_size.
The hash algorithm we used was xxHash - Extremely fast hash algorithm
(https://github.com/Cyan4973/xxHash/tree/v0.6.5)
Args:
input(Variable): A **Two-Dimensional** LoDTensor with type int32, int64.
**Only support LoDTensor**.
num_hash(int, optional): The times of hash, default is 1.
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: A LoDTensor with the same data type as input.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
import paddle
paddle.enable_static()
place = fluid.core.CPUPlace()
x = fluid.data(name="x", shape=[2,2], dtype="int32", lod_level=1)
res = fluid.layers.hash(name="res", input=x, hash_size=1000, num_hash=4)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
in1 = np.array([[1,2],[3,4]]).astype("int32")
print(in1)
x_i = fluid.create_lod_tensor(in1, [[0, 2]], place)
res = exe.run(fluid.default_main_program(), feed={'x':x_i}, fetch_list=[res], return_numpy=False)
print(np.array(res[0]))
# [[[722]
# [407]
# [337]
# [395]]
# [[603]
# [590]
# [386]
# [901]]]
"""
check_variable_and_dtype(input, 'input', ['int32', 'int64'], 'hash')
check_type(hash_size, 'hash_size', int, 'hash')
check_type(num_hash, 'num_hash', int, 'hash')
helper = LayerHelper('hash', **locals())
out = helper.create_variable_for_type_inference(
helper.input_dtype(), stop_gradient=True
)
helper.append_op(
type='hash',
inputs={'X': input},
outputs={'Out': out},
attrs={'num_hash': num_hash, 'mod_by': hash_size},
)
return out
@templatedoc()
def grid_sampler(x, grid, name=None):
"""
This operation samples input X by using bilinear interpolation based on
flow field grid, which is usually generated by :code:`affine_grid` . The grid of
shape [N, H, W, 2] is the concatenation of (x, y) coordinates
with shape [N, H, W] each, where x is indexing the 4th dimension
(in width dimension) of input data x and y is indexing the 3rd
dimension (in height dimension), finally results is the bilinear
interpolation value of 4 nearest corner points. The output tensor
shape will be [N, C, H, W].
.. code-block:: text
Step 1:
Get (x, y) grid coordinates and scale to [0, H-1/W-1].
.. code-block:: text
grid_x = 0.5 * (grid[:, :, :, 0] + 1) * (W - 1)
grid_y = 0.5 * (grid[:, :, :, 1] + 1) * (H - 1)
Step 2:
Indices input data X with grid (x, y) in each [H, W] area, and bilinear
interpolate point value by 4 nearest points.
wn ------- y_n ------- en
| | |
| d_n |
| | |
x_w --d_w-- grid--d_e-- x_e
| | |
| d_s |
| | |
ws ------- y_s ------- wn
x_w = floor(x) // west side x coord
x_e = x_w + 1 // east side x coord
y_n = floor(y) // north side y coord
y_s = y_s + 1 // south side y coord
d_w = grid_x - x_w // distance to west side
d_e = x_e - grid_x // distance to east side
d_n = grid_y - y_n // distance to north side
d_s = y_s - grid_y // distance to south side
wn = X[:, :, y_n, x_w] // north-west point value
en = X[:, :, y_n, x_e] // north-east point value
ws = X[:, :, y_s, x_w] // south-east point value
es = X[:, :, y_s, x_w] // north-east point value
output = wn * d_e * d_s + en * d_w * d_s
+ ws * d_e * d_n + es * d_w * d_n
Args:
x(Variable): The input tensor, which is a 4-D tensor with shape
[N, C, H, W], N is the batch size, C is the channel
number, H and W is the feature height and width.
The data type is float32 or float64.
grid(Variable): Input grid tensor of shape [N, H, W, 2]. The
data type is float32 or float64.
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
Returns:
Variable: Output of shape [N, C, H, W] data samples input X
using bilnear interpolation based on input grid.
The data type is same as input tensor.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid as fluid
import paddle
paddle.enable_static()
# use with affine_grid
x = fluid.data(name='x', shape=[None, 10, 32, 32], dtype='float32')
theta = fluid.layers.data(name='theta', shape=[2, 3], dtype='float32')
grid = fluid.layers.affine_grid(theta=theta, out_shape=[3, 10, 32, 32])
out = fluid.layers.grid_sampler(x=x, grid=grid)
"""
helper = LayerHelper("grid_sampler", **locals())
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'grid_sampler')
check_variable_and_dtype(
grid, 'grid', ['float32', 'float64'], 'grid_sampler'
)
if not isinstance(x, Variable):
return ValueError("The x should be a Variable")
if not isinstance(grid, Variable):
return ValueError("The grid should be a Variable")
out = helper.create_variable_for_type_inference(x.dtype)
ipts = {'X': x, 'Grid': grid}
attrs = {'use_cudnn': False} if core.is_compiled_with_rocm() else {}
helper.append_op(
type='grid_sampler', inputs=ipts, outputs={'Output': out}, attrs=attrs
)
return out
def log_loss(input, label, epsilon=1e-4, name=None):
r"""
**Negative Log Loss Layer**
This layer accepts input predictions and target label and returns the
negative log loss.
.. math::
Out = -label * \log{(input + \epsilon)}
- (1 - label) * \log{(1 - input + \epsilon)}
Args:
input (Tensor|list): A 2-D tensor with shape [N x 1], where N is the
batch size. This input is a probability computed
by the previous operator. Data type float32.
label (Tensor|list): The ground truth which is a 2-D tensor with
shape [N x 1], where N is the batch size.
Data type float32.
epsilon (float, optional): A small number for numerical stability. Default 1e-4.
name(str|None): For detailed information, please refer to
:ref:`api_guide_Name` . Usually name is no need to set and None by default.
Returns:
Tensor, which shape is [N x 1], data type is float32.
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
label = paddle.randn((10,1))
prob = paddle.randn((10,1))
cost = F.log_loss(input=prob, label=label)
"""
return paddle.nn.functional.log_loss(input, label, epsilon, name)
def bilinear_tensor_product(
x, y, size, act=None, name=None, param_attr=None, bias_attr=None
):
r"""
:api_attr: Static Graph
**Bilinear Tensor Product Layer**
This layer performs bilinear tensor product on two inputs.
For example:
.. math::
out_{i} = x * W_{i} * {y^\mathrm{T}}, i=0,1,...,size-1
In this formula:
- :math:`x`: the first input contains M elements, shape is [batch_size, M].
- :math:`y`: the second input contains N elements, shape is [batch_size, N].
- :math:`W_{i}`: the i-th learned weight, shape is [M, N].
- :math:`out_{i}`: the i-th element of out, shape is [batch_size, size].
- :math:`y^\mathrm{T}`: the transpose of :math:`y_{2}`.
Args:
x (Variable): 2-D input tensor with shape [batch_size, M]. Data type
is float32 or float64.
y (Variable): 2-D input tensor with shape [batch_size, N]. Data type
should be same as **x**.
size (int): The dimension of this layer.
act (str|None): Activation to be applied to the output of this layer. Default None.
name(str|None): For detailed information, please refer to
:ref:`api_guide_Name` . Usually name is no need to set and None by default.
param_attr (ParamAttr|None): To specify the weight parameter attribute.
Default: None, which means the default weight parameter property is
used. See usage for details in :ref:`api_fluid_ParamAttr` .
bias_attr (ParamAttr|None): To specify the bias parameter attribute.
Default: None, which means the default bias parameter property is
used. See usage for details in :ref:`api_fluid_ParamAttr` .
Returns:
Variable: A 2-D Tensor of shape [batch_size, size]. Data type is the same as input **x**.
Examples:
.. code-block:: python
import paddle
paddle.enable_static()
layer1 = paddle.static.data("t1", shape=[-1, 5], dtype="float32")
layer2 = paddle.static.data("t2", shape=[-1, 4], dtype="float32")
tensor = paddle.static.nn.bilinear_tensor_product(x=layer1, y=layer2, size=1000)
"""
helper = LayerHelper('bilinear_tensor_product', **locals())
dtype = helper.input_dtype('x')
param_shape = [size, x.shape[1], y.shape[1]]
w = helper.create_parameter(
attr=helper.param_attr, shape=param_shape, dtype=dtype, is_bias=False
)
out = helper.create_variable_for_type_inference(dtype=dtype)
inputs = {"X": x, "Y": y, "Weight": w}
if helper.bias_attr:
bias_size = [1, size]
bias = helper.create_parameter(
attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True
)
inputs["Bias"] = bias
helper.append_op(
type="bilinear_tensor_product", inputs=inputs, outputs={"Out": out}
)
# add activation
return helper.append_activation(out)
@templatedoc()
def get_tensor_from_selected_rows(x, name=None):
"""
......
python/paddle/fluid/tests/unittests/npu/test_log_loss_op_npu.py
浏览文件 @ cb812f40
......@@ -76,37 +76,5 @@ class TestLogLossOp(OpTest):
self.check_grad_with_place(self.place, ['Predicted'], 'Loss')
@unittest.skipIf(
not paddle.is_compiled_with_npu(), "core is not compiled with NPU"
)
class TestLogLossOpError(unittest.TestCase):
def test_errors(self):
with fluid.program_guard(fluid.Program()):
def test_x_type():
input_data = np.random.random(100, 1).astype("float32")
fluid.layers.log_loss(input_data)
self.assertRaises(TypeError, test_x_type)
def test_x_dtype():
x2 = fluid.layers.data(name='x2', shape=[100, 1], dtype='int32')
fluid.layers.log_loss(x2)
self.assertRaises(TypeError, test_x_dtype)
def test_label_type():
input_data = np.random.random(100, 1).astype("float32")
fluid.layers.log_loss(input_data)
self.assertRaises(TypeError, test_label_type)
def test_label_dtype():
x2 = fluid.layers.data(name='x2', shape=[100, 1], dtype='int32')
fluid.layers.log_loss(x2)
self.assertRaises(TypeError, test_label_dtype)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_fleet.py
浏览文件 @ cb812f40
......@@ -79,7 +79,7 @@ class TestFleet1(unittest.TestCase):
append_batch_size=False,
)
label_cast = fluid.layers.cast(label, dtype='float32')
cost = fluid.layers.log_loss(fc, label_cast)
cost = paddle.nn.functional.log_loss(fc, label_cast)
try:
adam = fluid.optimizer.Adam(learning_rate=0.000005)
adam = fleet.distributed_optimizer(
......
python/paddle/fluid/tests/unittests/test_fleet_nocvm_1.py
浏览文件 @ cb812f40
......@@ -16,6 +16,8 @@
import os
import unittest
import paddle
class TestFleet1(unittest.TestCase):
"""
......@@ -73,7 +75,7 @@ class TestFleet1(unittest.TestCase):
append_batch_size=False,
)
label_cast = fluid.layers.cast(label, dtype='float32')
cost = fluid.layers.log_loss(fc, label_cast)
cost = paddle.nn.functional.log_loss(fc, label_cast)
try:
adam = fluid.optimizer.Adam(learning_rate=0.000005)
adam = fleet.distributed_optimizer(
......
python/paddle/fluid/tests/unittests/test_fleet_rolemaker.py
浏览文件 @ cb812f40
......@@ -16,6 +16,7 @@
import os
import unittest
import paddle
import paddle.fluid.incubate.fleet.base.role_maker as role_maker
......@@ -97,7 +98,7 @@ class TestCloudRoleMaker(unittest.TestCase):
append_batch_size=False,
)
label_cast = fluid.layers.cast(label, dtype='float32')
cost = fluid.layers.log_loss(fc, label_cast)
cost = paddle.nn.functional.log_loss(fc, label_cast)
try:
adam = fluid.optimizer.Adam(learning_rate=0.000005)
adam = fleet.distributed_optimizer(adam)
......
python/paddle/fluid/tests/unittests/test_fleet_rolemaker_2.py
浏览文件 @ cb812f40
......@@ -79,7 +79,7 @@ class TestCloudRoleMaker2(unittest.TestCase):
append_batch_size=False,
)
label_cast = fluid.layers.cast(label, dtype='float32')
cost = fluid.layers.log_loss(fc, label_cast)
cost = paddle.nn.functional.log_loss(fc, label_cast)
try:
adam = fluid.optimizer.Adam(learning_rate=0.000005)
adam = fleet.distributed_optimizer(adam)
......
python/paddle/fluid/tests/unittests/test_fleet_rolemaker_3.py
浏览文件 @ cb812f40
......@@ -16,6 +16,8 @@
import os
import unittest
import paddle
class TestCloudRoleMaker(unittest.TestCase):
"""
......@@ -70,7 +72,7 @@ class TestCloudRoleMaker(unittest.TestCase):
append_batch_size=False,
)
label_cast = fluid.layers.cast(label, dtype='float32')
cost = fluid.layers.log_loss(fc, label_cast)
cost = paddle.nn.functional.log_loss(fc, label_cast)
try:
adam = fluid.optimizer.Adam(learning_rate=0.000005)
adam = fleet.distributed_optimizer(adam)
......
python/paddle/fluid/tests/unittests/test_fleet_unitaccessor.py
浏览文件 @ cb812f40
......@@ -16,6 +16,8 @@
import os
import unittest
import paddle
class TestFleet1(unittest.TestCase):
"""
......@@ -73,7 +75,7 @@ class TestFleet1(unittest.TestCase):
append_batch_size=False,
)
label_cast = fluid.layers.cast(label, dtype='float32')
cost = fluid.layers.log_loss(fc, label_cast)
cost = paddle.nn.functional.log_loss(fc, label_cast)
strategy = {}
strategy["embedding"] = {}
......
python/paddle/fluid/tests/unittests/test_hash_op.py
浏览文件 @ cb812f40
......@@ -17,8 +17,6 @@ import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid as fluid
class TestHashOp(OpTest):
def setUp(self):
......@@ -120,44 +118,5 @@ class TestHashOp3(TestHashOp):
self.check_output()
class TestHashOpError(unittest.TestCase):
def test_errors(self):
with fluid.program_guard(fluid.Program(), fluid.Program()):
input_data = np.random.randint(0, 10, (8, 1)).astype("int32")
def test_Variable():
# the input type must be Variable
fluid.layers.hash(input=input_data, hash_size=2**32)
self.assertRaises(TypeError, test_Variable)
def test_type():
# dtype must be int32, int64.
x2 = fluid.layers.data(
name='x2', shape=[1], dtype="float32", lod_level=1
)
fluid.layers.hash(input=x2, hash_size=2**32)
self.assertRaises(TypeError, test_type)
def test_hash_size_type():
# hash_size dtype must be int32, int64.
x3 = fluid.layers.data(
name='x3', shape=[1], dtype="int32", lod_level=1
)
fluid.layers.hash(input=x3, hash_size=1024.5)
self.assertRaises(TypeError, test_hash_size_type)
def test_num_hash_type():
# num_hash dtype must be int32, int64.
x4 = fluid.layers.data(
name='x4', shape=[1], dtype="int32", lod_level=1
)
fluid.layers.hash(input=x4, hash_size=2**32, num_hash=2.5)
self.assertRaises(TypeError, test_num_hash_type)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_imperative_deepcf.py
浏览文件 @ cb812f40
......@@ -271,7 +271,7 @@ class TestDygraphDeepCF(unittest.TestCase):
deepcf = DeepCF(num_users, num_items, matrix)
prediction = deepcf(users, items)
loss = paddle.sum(fluid.layers.log_loss(prediction, labels))
loss = paddle.sum(paddle.nn.functional.log_loss(prediction, labels))
adam = fluid.optimizer.AdamOptimizer(0.01)
adam.minimize(loss)
......@@ -325,7 +325,7 @@ class TestDygraphDeepCF(unittest.TestCase):
to_variable(items_np[slice : slice + self.batch_size]),
)
loss = paddle.sum(
fluid.layers.log_loss(
paddle.nn.functional.log_loss(
prediction,
to_variable(
labels_np[slice : slice + self.batch_size]
......@@ -359,7 +359,7 @@ class TestDygraphDeepCF(unittest.TestCase):
to_variable(items_np[slice : slice + self.batch_size]),
)
loss2 = paddle.sum(
fluid.layers.log_loss(
paddle.nn.functional.log_loss(
prediction2,
to_variable(
labels_np[slice : slice + self.batch_size]
......@@ -402,7 +402,7 @@ class TestDygraphDeepCF(unittest.TestCase):
),
)
loss = paddle.sum(
fluid.layers.log_loss(
paddle.nn.functional.log_loss(
prediction,
to_variable(
labels_np[slice : slice + self.batch_size]
......
python/paddle/fluid/tests/unittests/test_imperative_load_static_param.py
浏览文件 @ cb812f40
......@@ -86,11 +86,15 @@ class TestDygraphLoadStatic(unittest.TestCase):
"t2", shape=[None, 4], dtype="float32"
)
bilinear_tensor_pro_out_1 = fluid.layers.bilinear_tensor_product(
x=bilinear_tensor_pro_x, y=bilinear_tensor_pro_y, size=1000
)
bilinear_tensor_pro_out_2 = fluid.layers.bilinear_tensor_product(
x=bilinear_tensor_pro_x, y=bilinear_tensor_pro_y, size=1000
bilinear_tensor_pro_out_1 = (
paddle.static.nn.common.bilinear_tensor_product(
x=bilinear_tensor_pro_x, y=bilinear_tensor_pro_y, size=1000
)
)
bilinear_tensor_pro_out_2 = (
paddle.static.nn.common.bilinear_tensor_product(
x=bilinear_tensor_pro_x, y=bilinear_tensor_pro_y, size=1000
)
)
conv2d_trans_in = fluid.data(
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ cb812f40
......@@ -750,7 +750,7 @@ class TestLayer(LayerTest):
data_y = layers.data(
name='y', shape=[1, 3], dtype="float32", append_batch_size=False
)
out = layers.bilinear_tensor_product(
out = paddle.static.nn.common.bilinear_tensor_product(
data_x,
data_y,
6,
......@@ -825,7 +825,7 @@ class TestLayer(LayerTest):
data_y2 = layers.data(
name='y', shape=[1, 3], dtype="float32", append_batch_size=False
)
out2 = layers.bilinear_tensor_product(
out2 = paddle.static.nn.common.bilinear_tensor_product(
data_x2, data_y2, 6, act='sigmoid'
)
......@@ -3418,15 +3418,6 @@ class TestBook(LayerTest):
out = layers.iou_similarity(x, y, name='iou_similarity')
return out
def make_grid_sampler(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
x = self._get_data(name='x', shape=[3, 5, 7], dtype='float32')
grid = self._get_data(name='grid', shape=[5, 7, 2], dtype='float32')
out = layers.grid_sampler(x, grid)
return out
def make_bilinear_tensor_product_layer(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
......@@ -3434,7 +3425,9 @@ class TestBook(LayerTest):
data = self._get_data(name='data', shape=[4], dtype="float32")
theta = self._get_data(name="theta", shape=[5], dtype="float32")
out = layers.bilinear_tensor_product(data, theta, 6)
out = paddle.static.nn.common.bilinear_tensor_product(
data, theta, 6
)
return out
def make_batch_norm(self):
......
python/paddle/fluid/tests/unittests/test_log_loss_op.py
浏览文件 @ cb812f40
......@@ -17,8 +17,6 @@ import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid as fluid
def sigmoid_array(x):
return 1 / (1 + np.exp(-x))
......@@ -51,34 +49,5 @@ class TestLogLossOp(OpTest):
self.check_grad(['Predicted'], 'Loss', max_relative_error=0.03)
class TestLogLossOpError(unittest.TestCase):
def test_errors(self):
with fluid.program_guard(fluid.Program()):
def test_x_type():
input_data = np.random.random(100, 1).astype("float32")
fluid.layers.log_loss(input_data)
self.assertRaises(TypeError, test_x_type)
def test_x_dtype():
x2 = fluid.layers.data(name='x2', shape=[100, 1], dtype='int32')
fluid.layers.log_loss(x2)
self.assertRaises(TypeError, test_x_dtype)
def test_label_type():
input_data = np.random.random(100, 1).astype("float32")
fluid.layers.log_loss(input_data)
self.assertRaises(TypeError, test_label_type)
def test_label_dtype():
x2 = fluid.layers.data(name='x2', shape=[100, 1], dtype='int32')
fluid.layers.log_loss(x2)
self.assertRaises(TypeError, test_label_dtype)
if __name__ == '__main__':
unittest.main()
python/paddle/static/nn/__init__.py
浏览文件 @ cb812f40
......@@ -20,11 +20,11 @@ from .common import deform_conv2d # noqa: F401
from .common import conv3d # noqa: F401
from .common import conv2d_transpose # noqa: F401
from .common import conv3d_transpose # noqa: F401
from .common import bilinear_tensor_product # noqa: F401
from .common import py_func # noqa: F401
from ...tensor.creation import create_parameter # noqa: F401
from ...fluid.layers import batch_norm # noqa: F401
from ...fluid.layers import bilinear_tensor_product # noqa: F401
from ...fluid.layers import case # noqa: F401
from ...fluid.layers import cond # noqa: F401
from ...fluid.layers import conv2d # noqa: F401
......@@ -61,8 +61,8 @@ from ...fluid.layers.sequence_lod import sequence_reverse # noqa: F401
__all__ = [ # noqa
'fc',
'batch_norm',
'embedding',
'bilinear_tensor_product',
'embedding',
'case',
'cond',
'conv2d',
......
python/paddle/static/nn/common.py
浏览文件 @ cb812f40
......@@ -2088,6 +2088,184 @@ def deform_conv2d(
)
def bilinear_tensor_product(
x, y, size, act=None, name=None, param_attr=None, bias_attr=None
):
r"""
This layer performs bilinear tensor product on two inputs.
.. math::
out_{i} = x * W_{i} * {y^\mathrm{T}}, i=0,1,...,size-1
In this formula:
- :math:`x`: the first input contains M elements, shape is [batch_size, M].
- :math:`y`: the second input contains N elements, shape is [batch_size, N].
- :math:`W_{i}`: the i-th learned weight, shape is [M, N].
- :math:`out_{i}`: the i-th element of out, shape is [batch_size, size].
- :math:`y^\mathrm{T}`: the transpose of :math:`y_{2}`.
Args:
x (Variable): 2-D input tensor with shape [batch_size, M]. Data type
is float32 or float64.
y (Variable): 2-D input tensor with shape [batch_size, N]. Data type
should be same as **x**.
size (int): The dimension of this layer.
act (str|None): Activation to be applied to the output of this layer. Default None.
name(str|None): For detailed information, please refer to
:ref:`api_guide_Name` . Usually name is no need to set and None by default.
param_attr (ParamAttr|None): To specify the weight parameter attribute.
Default: None, which means the default weight parameter property is
used. See usage for details in :ref:`api_fluid_ParamAttr` .
bias_attr (ParamAttr|None): To specify the bias parameter attribute.
Default: None, which means the default bias parameter property is
used. See usage for details in :ref:`api_fluid_ParamAttr` .
Returns:
Tensor, A 2-D Tensor of shape [batch_size, size]. Data type is the same as input **x**.
Examples:
.. code-block:: python
import paddle
paddle.enable_static()
x = paddle.static.data("t1", shape=[-1, 5], dtype="float32")
y = paddle.static.data("t2", shape=[-1, 4], dtype="float32")
tensor = paddle.static.nn.bilinear_tensor_product(x, y, size=1000)
"""
helper = LayerHelper('bilinear_tensor_product', **locals())
dtype = helper.input_dtype('x')
param_shape = [size, x.shape[1], y.shape[1]]
w = helper.create_parameter(
attr=helper.param_attr, shape=param_shape, dtype=dtype, is_bias=False
)
out = helper.create_variable_for_type_inference(dtype=dtype)
inputs = {"X": x, "Y": y, "Weight": w}
if helper.bias_attr:
bias_size = [1, size]
bias = helper.create_parameter(
attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True
)
inputs["Bias"] = bias
helper.append_op(
type="bilinear_tensor_product", inputs=inputs, outputs={"Out": out}
)
# add activation
return helper.append_activation(out)
@static_only
def prelu(x, mode, param_attr=None, data_format="NCHW", name=None):
r"""
prelu activation.
.. math::
prelu(x) = max(0, x) + \alpha * min(0, x)
There are three modes for the activation:
.. code-block:: text
all: All elements share same alpha.
channel: Elements in same channel share same alpha.
element: All elements do not share alpha. Each element has its own alpha.
Parameters:
x (Tensor): The input Tensor or LoDTensor with data type float32.
mode (str): The mode for weight sharing.
param_attr (ParamAttr|None, optional): The parameter attribute for the learnable \
weight (alpha), it can be create by ParamAttr. None by default. \
For detailed information, please refer to :ref:`api_paddle_ParamAttr`.
data_format(str, optional): Data format that specifies the layout of input.
It may be "NC", "NCL", "NCHW", "NCDHW", "NLC", "NHWC" or "NDHWC". Default: "NCHW".
name (str, optional): Name for the operation (optional, default is None). \
For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor: A tensor with the same shape and data type as x.
Examples:
.. code-block:: python
import paddle
paddle.enable_static()
x = paddle.static.data(name="x", shape=[None,5,10,10], dtype="float32")
mode = 'channel'
output = paddle.static.nn.prelu(
x,mode,param_attr=paddle.ParamAttr(name='alpha'))
"""
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'prelu')
helper = LayerHelper('prelu', **locals())
if mode not in ['all', 'channel', 'element']:
raise ValueError('mode should be one of all, channel, element.')
alpha_shape = [1]
if mode == 'channel':
true_data_format = [
'NC',
'NCL',
'NCHW',
'NCDHW',
'NLC',
'NHWC',
'NDHWC',
]
if data_format not in true_data_format:
raise ValueError(
"data_format must be one of 'NC', 'NCL', 'NCHW', 'NCDHW', "
"'NLC', 'NHWC', 'NDHWC' but receive {}".format(data_format)
)
data_format = 'NCHW' if data_format[1] == 'C' else 'NHWC'
assert (
len(x.shape) >= 2
), "The size of input shape should be equal or larger than 2 in prelu() when mode is 'channel'"
# NOTE(zhiqiu): The alpha_shape should be [1, channel] + [1] * len(x.shape[2:]).
# To be consistent with Prelu, it is simplified.
# NOTE(zhiqiu): Revert shape to [1, channel, 1, 1] for compatibility with saved model of old version.
# NOTE(GuoxiaWang): support NHWC data format
if data_format == 'NHWC':
alpha_shape = [1, 1, 1, x.shape[-1]]
else:
alpha_shape = [1, x.shape[1], 1, 1]
elif mode == 'element':
assert (
len(x.shape) >= 1
), "The size of input shape should be equal or larger than 1 in prelu() when mode is 'element'"
alpha_shape = [1] + list(x.shape)[1:]
dtype = helper.input_dtype(input_param_name='x')
alpha = helper.create_parameter(
attr=helper.param_attr,
shape=alpha_shape,
dtype=dtype,
is_bias=False,
default_initializer=paddle.nn.initializer.Constant(0.25),
)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="prelu",
inputs={"X": x, 'Alpha': alpha},
attrs={"mode": mode, "data_format": data_format},
outputs={"Out": out},
)
return out
class PyFuncRegistry:
_register_funcs = []
......@@ -2106,12 +2284,10 @@ class PyFuncRegistry:
self._id = core._append_python_callable_object_and_return_id(self)
'''
Why record self here?
1. For debug usage. Users can call
:code:`py_func.registered_func(idx)` method
to find the registered function corresponding
to :code:`idx`.
2. For increasing reference count of self.
It seems that to release Python object
whose reference count is 1 would cause
......@@ -2169,25 +2345,20 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
This is used to register customized Python OP to Paddle. The design
principe of py_func is that Tensor and numpy array can be converted to each
other easily. So you can use Python and numpy API to register a python OP.
The forward function of the registered OP is ``func`` and the backward function
of that is ``backward_func``. Paddle will call ``func`` at forward runtime and
call ``backward_func`` at backward runtime(if ``backward_func`` is not None).
``x`` is the input of ``func``, whose type must be Tensor; ``out`` is
the output of ``func``, whose type can be either Tensor or numpy array.
The input of the backward function ``backward_func`` is ``x``, ``out`` and
the gradient of ``out``. If ``out`` have no gradient, the relevant input of
``backward_func`` is None. If ``x`` do not have a gradient, the user should
return None in ``backward_func``.
The data type and shape of ``out`` should also be set correctly before this
API is called, and the data type and shape of the gradient of ``out`` and
``x`` will be inferred automatically.
This API can also be used to debug the neural network by setting the ``func``
as a function that only print variables.
Args:
func (callable): The forward function of the registered OP. When the network
is running, the forward output ``out`` will be calculated according to this
......@@ -2211,61 +2382,47 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
that no tensors need to be removed from ``x`` and ``out``. If it is not None,
these tensors will not be the input of ``backward_func``. This parameter is only
useful when ``backward_func`` is not None.
Returns:
Tensor|tuple(Tensor)|list[Tensor]: The output ``out`` of the forward function ``func``.
Examples:
.. code-block:: python
# example 1:
import paddle
import numpy as np
paddle.enable_static()
# Creates a forward function, Tensor can be input directly without
# being converted into numpy array.
def tanh(x):
return np.tanh(x)
# Skip x in backward function and return the gradient of x
# Tensor must be actively converted to numpy array, otherwise,
# operations such as +/- can't be used.
def tanh_grad(y, dy):
return np.array(dy) * (1 - np.square(np.array(y)))
# Creates a forward function for debugging running networks(print value)
def debug_func(x):
print(x)
def create_tmp_var(name, dtype, shape):
return paddle.static.default_main_program().current_block().create_var(
name=name, dtype=dtype, shape=shape)
def simple_net(img, label):
hidden = img
for idx in range(4):
hidden = paddle.static.nn.fc(hidden, size=200)
new_hidden = create_tmp_var(name='hidden_{}'.format(idx),
dtype=hidden.dtype, shape=hidden.shape)
# User-defined forward and backward
hidden = paddle.static.py_func(func=tanh, x=hidden,
out=new_hidden, backward_func=tanh_grad,
skip_vars_in_backward_input=hidden)
# User-defined debug functions that print out the input Tensor
paddle.static.py_func(func=debug_func, x=hidden, out=None)
prediction = paddle.static.nn.fc(hidden, size=10, activation='softmax')
ce_loss = paddle.nn.loss.CrossEntropyLoss()
return ce_loss(prediction, label)
x = paddle.static.data(name='x', shape=[1,4], dtype='float32')
y = paddle.static.data(name='y', shape=[1], dtype='int64')
res = simple_net(x, y)
exe = paddle.static.Executor(paddle.CPUPlace())
exe.run(paddle.static.default_startup_program())
input1 = np.random.random(size=[1,4]).astype('float32')
......@@ -2274,54 +2431,40 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
feed={'x':input1, 'y':input2},
fetch_list=[res.name])
print(out)
.. code-block:: python
# example 2:
# This example shows how to turn Tensor into numpy array and
# use numpy API to register an Python OP
import paddle
import numpy as np
paddle.enable_static()
def element_wise_add(x, y):
# Tensor must be actively converted to numpy array, otherwise,
# numpy.shape can't be used.
x = np.array(x)
y = np.array(y)
if x.shape != y.shape:
raise AssertionError("the shape of inputs must be the same!")
result = np.zeros(x.shape, dtype='int32')
for i in range(len(x)):
for j in range(len(x[0])):
result[i][j] = x[i][j] + y[i][j]
return result
def create_tmp_var(name, dtype, shape):
return paddle.static.default_main_program().current_block().create_var(
name=name, dtype=dtype, shape=shape)
def py_func_demo():
start_program = paddle.static.default_startup_program()
main_program = paddle.static.default_main_program()
# Input of the forward function
x = paddle.static.data(name='x', shape=[2,3], dtype='int32')
y = paddle.static.data(name='y', shape=[2,3], dtype='int32')
# Output of the forward function, name/dtype/shape must be specified
output = create_tmp_var('output','int32', [3,1])
# Multiple Variable should be passed in the form of tuple(Variale) or list[Variale]
paddle.static.py_func(func=element_wise_add, x=[x,y], out=output)
exe=paddle.static.Executor(paddle.CPUPlace())
exe.run(start_program)
# Feed numpy array to main_program
input1 = np.random.randint(1, 10, size=[2,3], dtype='int32')
input2 = np.random.randint(1, 10, size=[2,3], dtype='int32')
......@@ -2329,9 +2472,7 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
feed={'x':input1, 'y':input2},
fetch_list=[output.name])
print("{0} + {1} = {2}".format(input1, input2, out))
py_func_demo()
# Reference output:
# [[5, 9, 9] + [[7, 8, 4] = [array([[12, 17, 13]
# [7, 5, 2]] [1, 3, 3]] [8, 8, 5]], dtype=int32)]
......@@ -2405,109 +2546,3 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
# For debug usage
py_func.registered_func = PyFuncRegistry.registered_func
py_func.registered_func_num = PyFuncRegistry.registered_func_num
@static_only
def prelu(x, mode, param_attr=None, data_format="NCHW", name=None):
r"""
prelu activation.
.. math::
prelu(x) = max(0, x) + \alpha * min(0, x)
There are three modes for the activation:
.. code-block:: text
all: All elements share same alpha.
channel: Elements in same channel share same alpha.
element: All elements do not share alpha. Each element has its own alpha.
Parameters:
x (Tensor): The input Tensor or LoDTensor with data type float32.
mode (str): The mode for weight sharing.
param_attr (ParamAttr|None, optional): The parameter attribute for the learnable \
weight (alpha), it can be create by ParamAttr. None by default. \
For detailed information, please refer to :ref:`api_paddle_ParamAttr`.
data_format(str, optional): Data format that specifies the layout of input.
It may be "NC", "NCL", "NCHW", "NCDHW", "NLC", "NHWC" or "NDHWC". Default: "NCHW".
name (str, optional): Name for the operation (optional, default is None). \
For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor: A tensor with the same shape and data type as x.
Examples:
.. code-block:: python
import paddle
paddle.enable_static()
x = paddle.static.data(name="x", shape=[None,5,10,10], dtype="float32")
mode = 'channel'
output = paddle.static.nn.prelu(
x,mode,param_attr=paddle.ParamAttr(name='alpha'))
"""
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'prelu')
helper = LayerHelper('prelu', **locals())
if mode not in ['all', 'channel', 'element']:
raise ValueError('mode should be one of all, channel, element.')
alpha_shape = [1]
if mode == 'channel':
true_data_format = [
'NC',
'NCL',
'NCHW',
'NCDHW',
'NLC',
'NHWC',
'NDHWC',
]
if data_format not in true_data_format:
raise ValueError(
"data_format must be one of 'NC', 'NCL', 'NCHW', 'NCDHW', "
"'NLC', 'NHWC', 'NDHWC' but receive {}".format(data_format)
)
data_format = 'NCHW' if data_format[1] == 'C' else 'NHWC'
assert (
len(x.shape) >= 2
), "The size of input shape should be equal or larger than 2 in prelu() when mode is 'channel'"
# NOTE(zhiqiu): The alpha_shape should be [1, channel] + [1] * len(x.shape[2:]).
# To be consistent with Prelu, it is simplified.
# NOTE(zhiqiu): Revert shape to [1, channel, 1, 1] for compatibility with saved model of old version.
# NOTE(GuoxiaWang): support NHWC data format
if data_format == 'NHWC':
alpha_shape = [1, 1, 1, x.shape[-1]]
else:
alpha_shape = [1, x.shape[1], 1, 1]
elif mode == 'element':
assert (
len(x.shape) >= 1
), "The size of input shape should be equal or larger than 1 in prelu() when mode is 'element'"
alpha_shape = [1] + list(x.shape)[1:]
dtype = helper.input_dtype(input_param_name='x')
alpha = helper.create_parameter(
attr=helper.param_attr,
shape=alpha_shape,
dtype=dtype,
is_bias=False,
default_initializer=paddle.nn.initializer.Constant(0.25),
)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="prelu",
inputs={"X": x, 'Alpha': alpha},
attrs={"mode": mode, "data_format": data_format},
outputs={"Out": out},
)
return out
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