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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__ = [ ...@@ -96,10 +96,6 @@ __all__ = [
'clip_by_norm', 'clip_by_norm',
'mean', 'mean',
'mul', 'mul',
'hash',
'grid_sampler',
'log_loss',
'bilinear_tensor_product',
'merge_selected_rows', 'merge_selected_rows',
'get_tensor_from_selected_rows', 'get_tensor_from_selected_rows',
'unfold', 'unfold',
...@@ -5223,292 +5219,6 @@ def mul(x, y, x_num_col_dims=1, y_num_col_dims=1, name=None): ...@@ -5223,292 +5219,6 @@ def mul(x, y, x_num_col_dims=1, y_num_col_dims=1, name=None):
return out 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() @templatedoc()
def get_tensor_from_selected_rows(x, name=None): 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): ...@@ -76,37 +76,5 @@ class TestLogLossOp(OpTest):
self.check_grad_with_place(self.place, ['Predicted'], 'Loss') 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__': if __name__ == '__main__':
unittest.main() unittest.main()
python/paddle/fluid/tests/unittests/test_fleet.py
浏览文件 @ cb812f40
...@@ -79,7 +79,7 @@ class TestFleet1(unittest.TestCase): ...@@ -79,7 +79,7 @@ class TestFleet1(unittest.TestCase):
append_batch_size=False, append_batch_size=False,
) )
label_cast = fluid.layers.cast(label, dtype='float32') 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: try:
adam = fluid.optimizer.Adam(learning_rate=0.000005) adam = fluid.optimizer.Adam(learning_rate=0.000005)
adam = fleet.distributed_optimizer( adam = fleet.distributed_optimizer(
......
python/paddle/fluid/tests/unittests/test_fleet_nocvm_1.py
浏览文件 @ cb812f40
...@@ -16,6 +16,8 @@ ...@@ -16,6 +16,8 @@
import os import os
import unittest import unittest
import paddle
class TestFleet1(unittest.TestCase): class TestFleet1(unittest.TestCase):
""" """
...@@ -73,7 +75,7 @@ class TestFleet1(unittest.TestCase): ...@@ -73,7 +75,7 @@ class TestFleet1(unittest.TestCase):
append_batch_size=False, append_batch_size=False,
) )
label_cast = fluid.layers.cast(label, dtype='float32') 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: try:
adam = fluid.optimizer.Adam(learning_rate=0.000005) adam = fluid.optimizer.Adam(learning_rate=0.000005)
adam = fleet.distributed_optimizer( adam = fleet.distributed_optimizer(
......
python/paddle/fluid/tests/unittests/test_fleet_rolemaker.py
浏览文件 @ cb812f40
...@@ -16,6 +16,7 @@ ...@@ -16,6 +16,7 @@
import os import os
import unittest import unittest
import paddle
import paddle.fluid.incubate.fleet.base.role_maker as role_maker import paddle.fluid.incubate.fleet.base.role_maker as role_maker
...@@ -97,7 +98,7 @@ class TestCloudRoleMaker(unittest.TestCase): ...@@ -97,7 +98,7 @@ class TestCloudRoleMaker(unittest.TestCase):
append_batch_size=False, append_batch_size=False,
) )
label_cast = fluid.layers.cast(label, dtype='float32') 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: try:
adam = fluid.optimizer.Adam(learning_rate=0.000005) adam = fluid.optimizer.Adam(learning_rate=0.000005)
adam = fleet.distributed_optimizer(adam) 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): ...@@ -79,7 +79,7 @@ class TestCloudRoleMaker2(unittest.TestCase):
append_batch_size=False, append_batch_size=False,
) )
label_cast = fluid.layers.cast(label, dtype='float32') 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: try:
adam = fluid.optimizer.Adam(learning_rate=0.000005) adam = fluid.optimizer.Adam(learning_rate=0.000005)
adam = fleet.distributed_optimizer(adam) adam = fleet.distributed_optimizer(adam)
......
python/paddle/fluid/tests/unittests/test_fleet_rolemaker_3.py
浏览文件 @ cb812f40
...@@ -16,6 +16,8 @@ ...@@ -16,6 +16,8 @@
import os import os
import unittest import unittest
import paddle
class TestCloudRoleMaker(unittest.TestCase): class TestCloudRoleMaker(unittest.TestCase):
""" """
...@@ -70,7 +72,7 @@ class TestCloudRoleMaker(unittest.TestCase): ...@@ -70,7 +72,7 @@ class TestCloudRoleMaker(unittest.TestCase):
append_batch_size=False, append_batch_size=False,
) )
label_cast = fluid.layers.cast(label, dtype='float32') 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: try:
adam = fluid.optimizer.Adam(learning_rate=0.000005) adam = fluid.optimizer.Adam(learning_rate=0.000005)
adam = fleet.distributed_optimizer(adam) adam = fleet.distributed_optimizer(adam)
......
python/paddle/fluid/tests/unittests/test_fleet_unitaccessor.py
浏览文件 @ cb812f40
...@@ -16,6 +16,8 @@ ...@@ -16,6 +16,8 @@
import os import os
import unittest import unittest
import paddle
class TestFleet1(unittest.TestCase): class TestFleet1(unittest.TestCase):
""" """
...@@ -73,7 +75,7 @@ class TestFleet1(unittest.TestCase): ...@@ -73,7 +75,7 @@ class TestFleet1(unittest.TestCase):
append_batch_size=False, append_batch_size=False,
) )
label_cast = fluid.layers.cast(label, dtype='float32') 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 = {}
strategy["embedding"] = {} strategy["embedding"] = {}
......
python/paddle/fluid/tests/unittests/test_hash_op.py
浏览文件 @ cb812f40
...@@ -17,8 +17,6 @@ import unittest ...@@ -17,8 +17,6 @@ import unittest
import numpy as np import numpy as np
from op_test import OpTest from op_test import OpTest
import paddle.fluid as fluid
class TestHashOp(OpTest): class TestHashOp(OpTest):
def setUp(self): def setUp(self):
...@@ -120,44 +118,5 @@ class TestHashOp3(TestHashOp): ...@@ -120,44 +118,5 @@ class TestHashOp3(TestHashOp):
self.check_output() 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__": if __name__ == "__main__":
unittest.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): ...@@ -271,7 +271,7 @@ class TestDygraphDeepCF(unittest.TestCase):
deepcf = DeepCF(num_users, num_items, matrix) deepcf = DeepCF(num_users, num_items, matrix)
prediction = deepcf(users, items) 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 = fluid.optimizer.AdamOptimizer(0.01)
adam.minimize(loss) adam.minimize(loss)
...@@ -325,7 +325,7 @@ class TestDygraphDeepCF(unittest.TestCase): ...@@ -325,7 +325,7 @@ class TestDygraphDeepCF(unittest.TestCase):
to_variable(items_np[slice : slice + self.batch_size]), to_variable(items_np[slice : slice + self.batch_size]),
) )
loss = paddle.sum( loss = paddle.sum(
fluid.layers.log_loss( paddle.nn.functional.log_loss(
prediction, prediction,
to_variable( to_variable(
labels_np[slice : slice + self.batch_size] labels_np[slice : slice + self.batch_size]
...@@ -359,7 +359,7 @@ class TestDygraphDeepCF(unittest.TestCase): ...@@ -359,7 +359,7 @@ class TestDygraphDeepCF(unittest.TestCase):
to_variable(items_np[slice : slice + self.batch_size]), to_variable(items_np[slice : slice + self.batch_size]),
) )
loss2 = paddle.sum( loss2 = paddle.sum(
fluid.layers.log_loss( paddle.nn.functional.log_loss(
prediction2, prediction2,
to_variable( to_variable(
labels_np[slice : slice + self.batch_size] labels_np[slice : slice + self.batch_size]
...@@ -402,7 +402,7 @@ class TestDygraphDeepCF(unittest.TestCase): ...@@ -402,7 +402,7 @@ class TestDygraphDeepCF(unittest.TestCase):
), ),
) )
loss = paddle.sum( loss = paddle.sum(
fluid.layers.log_loss( paddle.nn.functional.log_loss(
prediction, prediction,
to_variable( to_variable(
labels_np[slice : slice + self.batch_size] 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): ...@@ -86,11 +86,15 @@ class TestDygraphLoadStatic(unittest.TestCase):
"t2", shape=[None, 4], dtype="float32" "t2", shape=[None, 4], dtype="float32"
) )
bilinear_tensor_pro_out_1 = fluid.layers.bilinear_tensor_product( bilinear_tensor_pro_out_1 = (
x=bilinear_tensor_pro_x, y=bilinear_tensor_pro_y, size=1000 paddle.static.nn.common.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_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( 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): ...@@ -750,7 +750,7 @@ class TestLayer(LayerTest):
data_y = layers.data( data_y = layers.data(
name='y', shape=[1, 3], dtype="float32", append_batch_size=False 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_x,
data_y, data_y,
6, 6,
...@@ -825,7 +825,7 @@ class TestLayer(LayerTest): ...@@ -825,7 +825,7 @@ class TestLayer(LayerTest):
data_y2 = layers.data( data_y2 = layers.data(
name='y', shape=[1, 3], dtype="float32", append_batch_size=False 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' data_x2, data_y2, 6, act='sigmoid'
) )
...@@ -3418,15 +3418,6 @@ class TestBook(LayerTest): ...@@ -3418,15 +3418,6 @@ class TestBook(LayerTest):
out = layers.iou_similarity(x, y, name='iou_similarity') out = layers.iou_similarity(x, y, name='iou_similarity')
return out 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): def make_bilinear_tensor_product_layer(self):
with program_guard( with program_guard(
fluid.default_main_program(), fluid.default_startup_program() fluid.default_main_program(), fluid.default_startup_program()
...@@ -3434,7 +3425,9 @@ class TestBook(LayerTest): ...@@ -3434,7 +3425,9 @@ class TestBook(LayerTest):
data = self._get_data(name='data', shape=[4], dtype="float32") data = self._get_data(name='data', shape=[4], dtype="float32")
theta = self._get_data(name="theta", shape=[5], 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 return out
def make_batch_norm(self): 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 ...@@ -17,8 +17,6 @@ import unittest
import numpy as np import numpy as np
from op_test import OpTest from op_test import OpTest
import paddle.fluid as fluid
def sigmoid_array(x): def sigmoid_array(x):
return 1 / (1 + np.exp(-x)) return 1 / (1 + np.exp(-x))
...@@ -51,34 +49,5 @@ class TestLogLossOp(OpTest): ...@@ -51,34 +49,5 @@ class TestLogLossOp(OpTest):
self.check_grad(['Predicted'], 'Loss', max_relative_error=0.03) 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__': if __name__ == '__main__':
unittest.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 ...@@ -20,11 +20,11 @@ from .common import deform_conv2d # noqa: F401
from .common import conv3d # noqa: F401 from .common import conv3d # noqa: F401
from .common import conv2d_transpose # noqa: F401 from .common import conv2d_transpose # noqa: F401
from .common import conv3d_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 .common import py_func # noqa: F401
from ...tensor.creation import create_parameter # noqa: F401 from ...tensor.creation import create_parameter # noqa: F401
from ...fluid.layers import batch_norm # 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 case # noqa: F401
from ...fluid.layers import cond # noqa: F401 from ...fluid.layers import cond # noqa: F401
from ...fluid.layers import conv2d # noqa: F401 from ...fluid.layers import conv2d # noqa: F401
...@@ -61,8 +61,8 @@ from ...fluid.layers.sequence_lod import sequence_reverse # noqa: F401 ...@@ -61,8 +61,8 @@ from ...fluid.layers.sequence_lod import sequence_reverse # noqa: F401
__all__ = [ # noqa __all__ = [ # noqa
'fc', 'fc',
'batch_norm', 'batch_norm',
'embedding',
'bilinear_tensor_product', 'bilinear_tensor_product',
'embedding',
'case', 'case',
'cond', 'cond',
'conv2d', 'conv2d',
......
python/paddle/static/nn/common.py
浏览文件 @ cb812f40
...@@ -2088,6 +2088,184 @@ def deform_conv2d( ...@@ -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: class PyFuncRegistry:
_register_funcs = [] _register_funcs = []
...@@ -2106,12 +2284,10 @@ class PyFuncRegistry: ...@@ -2106,12 +2284,10 @@ class PyFuncRegistry:
self._id = core._append_python_callable_object_and_return_id(self) self._id = core._append_python_callable_object_and_return_id(self)
''' '''
Why record self here? Why record self here?
1. For debug usage. Users can call 1. For debug usage. Users can call
:code:`py_func.registered_func(idx)` method :code:`py_func.registered_func(idx)` method
to find the registered function corresponding to find the registered function corresponding
to :code:`idx`. to :code:`idx`.
2. For increasing reference count of self. 2. For increasing reference count of self.
It seems that to release Python object It seems that to release Python object
whose reference count is 1 would cause 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): ...@@ -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 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 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. 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 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 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). 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 ``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 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 input of the backward function ``backward_func`` is ``x``, ``out`` and
the gradient of ``out``. If ``out`` have no gradient, the relevant input of 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 ``backward_func`` is None. If ``x`` do not have a gradient, the user should
return None in ``backward_func``. return None in ``backward_func``.
The data type and shape of ``out`` should also be set correctly before this 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 API is called, and the data type and shape of the gradient of ``out`` and
``x`` will be inferred automatically. ``x`` will be inferred automatically.
This API can also be used to debug the neural network by setting the ``func`` This API can also be used to debug the neural network by setting the ``func``
as a function that only print variables. as a function that only print variables.
Args: Args:
func (callable): The forward function of the registered OP. When the network func (callable): The forward function of the registered OP. When the network
is running, the forward output ``out`` will be calculated according to this 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): ...@@ -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, 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 these tensors will not be the input of ``backward_func``. This parameter is only
useful when ``backward_func`` is not None. useful when ``backward_func`` is not None.
Returns: Returns:
Tensor|tuple(Tensor)|list[Tensor]: The output ``out`` of the forward function ``func``. Tensor|tuple(Tensor)|list[Tensor]: The output ``out`` of the forward function ``func``.
Examples: Examples:
.. code-block:: python .. code-block:: python
# example 1: # example 1:
import paddle import paddle
import numpy as np import numpy as np
paddle.enable_static() paddle.enable_static()
# Creates a forward function, Tensor can be input directly without # Creates a forward function, Tensor can be input directly without
# being converted into numpy array. # being converted into numpy array.
def tanh(x): def tanh(x):
return np.tanh(x) return np.tanh(x)
# Skip x in backward function and return the gradient of x # Skip x in backward function and return the gradient of x
# Tensor must be actively converted to numpy array, otherwise, # Tensor must be actively converted to numpy array, otherwise,
# operations such as +/- can't be used. # operations such as +/- can't be used.
def tanh_grad(y, dy): def tanh_grad(y, dy):
return np.array(dy) * (1 - np.square(np.array(y))) return np.array(dy) * (1 - np.square(np.array(y)))
# Creates a forward function for debugging running networks(print value) # Creates a forward function for debugging running networks(print value)
def debug_func(x): def debug_func(x):
print(x) print(x)
def create_tmp_var(name, dtype, shape): def create_tmp_var(name, dtype, shape):
return paddle.static.default_main_program().current_block().create_var( return paddle.static.default_main_program().current_block().create_var(
name=name, dtype=dtype, shape=shape) name=name, dtype=dtype, shape=shape)
def simple_net(img, label): def simple_net(img, label):
hidden = img hidden = img
for idx in range(4): for idx in range(4):
hidden = paddle.static.nn.fc(hidden, size=200) hidden = paddle.static.nn.fc(hidden, size=200)
new_hidden = create_tmp_var(name='hidden_{}'.format(idx), new_hidden = create_tmp_var(name='hidden_{}'.format(idx),
dtype=hidden.dtype, shape=hidden.shape) dtype=hidden.dtype, shape=hidden.shape)
# User-defined forward and backward # User-defined forward and backward
hidden = paddle.static.py_func(func=tanh, x=hidden, hidden = paddle.static.py_func(func=tanh, x=hidden,
out=new_hidden, backward_func=tanh_grad, out=new_hidden, backward_func=tanh_grad,
skip_vars_in_backward_input=hidden) skip_vars_in_backward_input=hidden)
# User-defined debug functions that print out the input Tensor # User-defined debug functions that print out the input Tensor
paddle.static.py_func(func=debug_func, x=hidden, out=None) paddle.static.py_func(func=debug_func, x=hidden, out=None)
prediction = paddle.static.nn.fc(hidden, size=10, activation='softmax') prediction = paddle.static.nn.fc(hidden, size=10, activation='softmax')
ce_loss = paddle.nn.loss.CrossEntropyLoss() ce_loss = paddle.nn.loss.CrossEntropyLoss()
return ce_loss(prediction, label) return ce_loss(prediction, label)
x = paddle.static.data(name='x', shape=[1,4], dtype='float32') x = paddle.static.data(name='x', shape=[1,4], dtype='float32')
y = paddle.static.data(name='y', shape=[1], dtype='int64') y = paddle.static.data(name='y', shape=[1], dtype='int64')
res = simple_net(x, y) res = simple_net(x, y)
exe = paddle.static.Executor(paddle.CPUPlace()) exe = paddle.static.Executor(paddle.CPUPlace())
exe.run(paddle.static.default_startup_program()) exe.run(paddle.static.default_startup_program())
input1 = np.random.random(size=[1,4]).astype('float32') 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): ...@@ -2274,54 +2431,40 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
feed={'x':input1, 'y':input2}, feed={'x':input1, 'y':input2},
fetch_list=[res.name]) fetch_list=[res.name])
print(out) print(out)
.. code-block:: python .. code-block:: python
# example 2: # example 2:
# This example shows how to turn Tensor into numpy array and # This example shows how to turn Tensor into numpy array and
# use numpy API to register an Python OP # use numpy API to register an Python OP
import paddle import paddle
import numpy as np import numpy as np
paddle.enable_static() paddle.enable_static()
def element_wise_add(x, y): def element_wise_add(x, y):
# Tensor must be actively converted to numpy array, otherwise, # Tensor must be actively converted to numpy array, otherwise,
# numpy.shape can't be used. # numpy.shape can't be used.
x = np.array(x) x = np.array(x)
y = np.array(y) y = np.array(y)
if x.shape != y.shape: if x.shape != y.shape:
raise AssertionError("the shape of inputs must be the same!") raise AssertionError("the shape of inputs must be the same!")
result = np.zeros(x.shape, dtype='int32') result = np.zeros(x.shape, dtype='int32')
for i in range(len(x)): for i in range(len(x)):
for j in range(len(x[0])): for j in range(len(x[0])):
result[i][j] = x[i][j] + y[i][j] result[i][j] = x[i][j] + y[i][j]
return result return result
def create_tmp_var(name, dtype, shape): def create_tmp_var(name, dtype, shape):
return paddle.static.default_main_program().current_block().create_var( return paddle.static.default_main_program().current_block().create_var(
name=name, dtype=dtype, shape=shape) name=name, dtype=dtype, shape=shape)
def py_func_demo(): def py_func_demo():
start_program = paddle.static.default_startup_program() start_program = paddle.static.default_startup_program()
main_program = paddle.static.default_main_program() main_program = paddle.static.default_main_program()
# Input of the forward function # Input of the forward function
x = paddle.static.data(name='x', shape=[2,3], dtype='int32') x = paddle.static.data(name='x', shape=[2,3], dtype='int32')
y = paddle.static.data(name='y', 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 of the forward function, name/dtype/shape must be specified
output = create_tmp_var('output','int32', [3,1]) output = create_tmp_var('output','int32', [3,1])
# Multiple Variable should be passed in the form of tuple(Variale) or list[Variale] # 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) paddle.static.py_func(func=element_wise_add, x=[x,y], out=output)
exe=paddle.static.Executor(paddle.CPUPlace()) exe=paddle.static.Executor(paddle.CPUPlace())
exe.run(start_program) exe.run(start_program)
# Feed numpy array to main_program # Feed numpy array to main_program
input1 = np.random.randint(1, 10, size=[2,3], dtype='int32') input1 = np.random.randint(1, 10, size=[2,3], dtype='int32')
input2 = 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): ...@@ -2329,9 +2472,7 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
feed={'x':input1, 'y':input2}, feed={'x':input1, 'y':input2},
fetch_list=[output.name]) fetch_list=[output.name])
print("{0} + {1} = {2}".format(input1, input2, out)) print("{0} + {1} = {2}".format(input1, input2, out))
py_func_demo() py_func_demo()
# Reference output: # Reference output:
# [[5, 9, 9] + [[7, 8, 4] = [array([[12, 17, 13] # [[5, 9, 9] + [[7, 8, 4] = [array([[12, 17, 13]
# [7, 5, 2]] [1, 3, 3]] [8, 8, 5]], dtype=int32)] # [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): ...@@ -2405,109 +2546,3 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
# For debug usage # For debug usage
py_func.registered_func = PyFuncRegistry.registered_func py_func.registered_func = PyFuncRegistry.registered_func
py_func.registered_func_num = PyFuncRegistry.registered_func_num 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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