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未验证 提交 da357615 编写于 作者: Z zqw_1997 提交者： GitHub
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remove fluid.contrib.fused_elemwise_activation, sequence_topk_avg_pooling,... 

remove fluid.contrib.fused_elemwise_activation, sequence_topk_avg_pooling, var_conv_2d, match_matrix_tensor and tree_conv (#49331)
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python/paddle/fluid/contrib/layers/nn.py
浏览文件 @ da357615
......@@ -41,11 +41,6 @@ import warnings
from paddle import _C_ops, _legacy_C_ops
__all__ = [
'fused_elemwise_activation',
'sequence_topk_avg_pooling',
'var_conv_2d',
'match_matrix_tensor',
'tree_conv',
'fused_embedding_seq_pool',
'multiclass_nms2',
'search_pyramid_hash',
......@@ -65,416 +60,6 @@ __all__ = [
]
def fused_elemwise_activation(
x, y, functor_list, axis=-1, scale=0.0, save_intermediate_out=True
):
"""
**Fused elementwise_add/mul and activation layers**
This function computes an elementwise_add/mul cooperated with an activation.
.. math::
out = Unary(Binary(x, y))
or
.. math::
out = Binary(x, Unary(y))
Unary operators can be: `scale`, `relu`, `tanh`. Binary operators can be:
`elementwise_add`, `elementwise_mul`.
Args:
x (Variable): left operation of the binary operator.
y (Variable): right operator of the binary operator.
functor_list (list of str): types of operator which will be executed
by this layer. For example, ['elementwise_add', 'relu']
(out = elementwise_add(x, relu(y))),
or ['relu', 'elemmentwise_add'] (out = relu(elementwise_add(x, y))).
axis (int32, default -1): axis of elementwise op.
scale (float32, default 0): parameter of scale op.
save_intermediate_out (bool, default True): whether to save the
intermediate result, Unary(y) or Binary(x, y).
Returns:
Variable: The computation result.
"""
if isinstance(functor_list, str):
functor_list = functor_list.split(',')
if not isinstance(functor_list, list) or len(functor_list) != 2:
raise ValueError(
'functor_list should be a list of str, and the length should be 2.'
)
helper = LayerHelper('fused_elemwise_activation', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
intermediate_out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='fused_elemwise_activation',
inputs={'X': x, 'Y': y},
outputs={'Out': out, 'IntermediateOut': intermediate_out},
attrs={
'axis': axis,
'scale': scale,
'save_intermediate_out': save_intermediate_out,
'functor_list': functor_list,
},
)
return out
def var_conv_2d(
input,
row,
col,
input_channel,
output_channel,
filter_size,
stride=1,
param_attr=None,
act=None,
dtype='float32',
name=None,
):
r"""
The var_conv_2d layer calculates the output base on the :attr:`input` with variable length,
row, col, input channel, filter size and strides. Both :attr:`input`, :attr:`row`,
and :attr:`col` are 1-level LodTensor. The convolution operation is same as conv2d layer with
padding. Besides, input.dims[1] should be 1.
.. code-block:: text
If input_channel is 2 and given row lodTensor and col lodTensor as follows:
row.lod = [[5, 4]]
col.lod = [[6, 7]]
input is a lodTensor:
input.lod = [[60, 56]] # where 60 = input_channel * 5 * 6
input.dims = [116, 1] # where 116 = 60 + 56
If set output_channel is 3, filter_size is [3, 3], stride is [1, 1]:
# where 90 = output_channel * [(5-1)/stride + 1] * [(6-1)/stride + 1]
output.lod = [[90, 84]]
output.dims = [174, 1] # where 174 = 90 + 84
Args:
input (Variable): The input should be 1-level LodTensor with dims[1] equals 1.
row (Variable): The row should be 1-level LodTensor to provide height information.
col (Variable): The col should be 1-level LodTensor to provide width information.
input_channel (int): The number of input channel.
output_channel (int): The number of output channel.
filter_size (int|tuple|None): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_H, filter_size_W).
Otherwise, the filter will be a square.
stride (int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride. Default: stride = 1.
param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
of var_conv2d. If it is set to None or one attribute of ParamAttr, var_conv2d
will create ParamAttr as param_attr. If the Initializer of the param_attr
is not set, the parameter is initialized with :math:`Normal(0.0, std)`,
and the :math:`std` is :math:`(\\frac{2.0 }{filter\_elem\_num})^{
0.5}`. Default: None.
act (str): Activation type, if it is set to None, activation is not appended.
Default: None
dtype ('float32'): The data type of parameter and output.
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically. Default: None
Returns:
Variable: Output variable with LoD specified by this layer.
Examples:
.. code-block:: python
import numpy as np
from paddle.fluid import layers
from paddle.fluid import contrib
x_lod_tensor = layers.data(name='x', shape=[1], lod_level=1)
row_lod_tensor = layers.data(name='row', shape=[6], lod_level=1)
col_lod_tensor = layers.data(name='col', shape=[6], lod_level=1)
out = contrib.var_conv_2d(input=x_lod_tensor,
row=row_lod_tensor,
col=col_lod_tensor,
input_channel=3,
output_channel=5,
filter_size=[3, 3],
stride=1)
"""
helper = LayerHelper('var_conv_2d', **locals())
x_shape = list(input.shape)
assert len(x_shape) == 2
filter_size = utils.convert_to_list(filter_size, 2, 'filter_size')
stride = utils.convert_to_list(stride, 2, 'stride')
filter_shape = [
int(output_channel),
int(input_channel) * filter_size[0] * filter_size[1],
]
filter_param = helper.create_parameter(
attr=helper.param_attr,
shape=filter_shape,
dtype=dtype,
)
conv_res = helper.create_variable_for_type_inference(dtype)
tmp_res = helper.create_variable_for_type_inference(
dtype, stop_gradient=True
)
helper.append_op(
type='var_conv_2d',
inputs={
'X': input,
'ROW': row,
'COLUMN': col,
'W': filter_param,
},
outputs={"Out": conv_res, "Col": tmp_res},
attrs={
'InputChannel': input_channel,
'OutputChannel': output_channel,
'StrideH': stride[0],
'StrideW': stride[1],
'KernelH': filter_size[0],
'KernelW': filter_size[1],
},
)
return helper.append_activation(conv_res)
def match_matrix_tensor(
x, y, channel_num, act=None, param_attr=None, dtype='float32', name=None
):
"""
Calculate the semantic matching matrix of two word sequences with variable length.
Given a query A of length `n` and a title B of length `m`, the input shape are respectively
[n, h] and [m, h], which h is hidden_size. If :attr:`channel_num` is set to 3,
it will generate a learnable parameter matrix W with shape [h, 3, h].
Then the semantic matching matrix of query A and title B is calculated by
A * W * B.T = [n, h]*[h, 3, h]*[h, m] = [n, 3, m]. The learnable parameter matrix `W`
is equivalent to a fully connected layer in the calculation process. If :attr:`act` is provided,
the corresponding activation function will be applied to output matrix.
The :attr:`x` and :attr:`y` should be LodTensor and only one level LoD is supported.
.. code-block:: text
Given a 1-level LoDTensor x:
x.lod = [
[2, 3, ]]
x.data = [[0.3, 0.1], [0.2, 0.3], [
0.5, 0.6], [0.7, 0.1], [0.3, 0.4]]
x.dims = [5, 2]
y is a Tensor:
y.lod = [[3, 1, ]]
y.data = [[0.1, 0.2], [0.3, 0.7], [0.9, 0.2], [0.4, 0.1]]
y.dims = [4, 2]
set channel_num 2, then we get a 1-level LoDTensor:
# where 12 = channel_num * x.lod[0][0] * y.lod[0][0]
out.lod = [[12, 6]]
out.dims = [18, 1] # where 18 = 12 + 6
Args:
x (Variable): Input variable x which should be 1-level LodTensor.
y (Variable): Input variable y which should be 1-level LodTensor.
channel_num (int): The channel number of learnable parameter W.
act (str, default None): Activation to be applied to the output of this layer.
param_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for learnable
parameters/weights of this layer.
dtype ('float32'): The data type of w data.
name (str|None): A name for this layer(optional). If set None, the layer will be named automatically. Default: None
Returns:
Variable: output with LoD specified by this layer.
Examples:
.. code-block:: python
import numpy as np
from paddle.fluid import layers
from paddle.fluid import contrib
x_lod_tensor = layers.data(name='x', shape=[10], lod_level=1)
y_lod_tensor = layers.data(name='y', shape=[10], lod_level=1)
out, out_tmp = contrib.match_matrix_tensor(
x=x_lod_tensor, y=y_lod_tensor, channel_num=3)
"""
helper = LayerHelper('match_matrix_tensor', **locals())
x_shape = list(x.shape)
y_shape = list(y.shape)
assert (
len(x_shape) == 2 and len(y_shape) == 2 and x_shape[-1] == y_shape[-1]
)
weight_shape = [x_shape[-1], channel_num, y_shape[-1]]
w = helper.create_parameter(
attr=helper.param_attr, shape=weight_shape, dtype=dtype, is_bias=False
)
mm_res = helper.create_variable_for_type_inference(dtype)
tmp_res = helper.create_variable_for_type_inference(
dtype, stop_gradient=True
)
helper.append_op(
type='match_matrix_tensor',
inputs={
'X': x,
'Y': y,
'W': w,
},
outputs={"Out": mm_res, "Tmp": tmp_res},
attrs={'dim_t': channel_num},
)
return helper.append_activation(mm_res), tmp_res
def sequence_topk_avg_pooling(input, row, col, topks, channel_num):
"""
The :attr:`topks` is a list with incremental values in this function. For each topk,
it will average the topk features as an output feature for each channel of every
input sequence. Both :attr:`row` and :attr:`col` are LodTensor, which provide height
and width information for :attr:`input` tensor. If feature size of input sequence is less
than topk, it will padding 0 at the back.
.. code-block:: text
If channel_num is 2 and given row LoDTensor and col LoDTensor as follows:
row.lod = [[5, 4]]
col.lod = [[6, 7]]
input is a LoDTensor with input.lod[0][i] = channel_num * row.lod[0][i] * col.lod[0][i]
input.lod = [[60, 56]] # where 60 = channel_num * 5 * 6
input.dims = [116, 1] # where 116 = 60 + 56
If topks is [1, 3, 5], then we get a 1-level LoDTensor:
out.lod = [[5, 4]] # share Lod info with row LodTensor
out.dims = [9, 6] # where 6 = len(topks) * channel_num
Args:
input (Variable): The input should be 2D LodTensor with dims[1] equals 1.
row (Variable): The row should be 1-level LodTensor to provide the height information
of the input tensor data.
col (Variable): The col should be 1-level LodTensor to provide the width information
of the input tensor data.
topks (list): A list of incremental value to average the topk feature.
channel_num (int): The number of input channel.
Returns:
Variable: output LodTensor specified by this layer.
Examples:
.. code-block:: python
import numpy as np
from paddle.fluid import layers
from paddle.fluid import contrib
x_lod_tensor = layers.data(name='x', shape=[1], lod_level=1)
row_lod_tensor = layers.data(name='row', shape=[6], lod_level=1)
col_lod_tensor = layers.data(name='col', shape=[6], lod_level=1)
out = contrib.sequence_topk_avg_pooling(input=x_lod_tensor,
row=row_lod_tensor,
col=col_lod_tensor,
topks=[1, 3, 5],
channel_num=5)
"""
helper = LayerHelper('sequence_topk_avg_pooling', **locals())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
pos = helper.create_variable_for_type_inference(
dtype=helper.input_dtype(), stop_gradient=True
)
helper.append_op(
type='sequence_topk_avg_pooling',
inputs={'X': input, 'ROW': row, 'COLUMN': col},
outputs={'Out': out, 'pos': pos},
attrs={'topks': topks, 'channel_num': channel_num},
)
return out
def tree_conv(
nodes_vector,
edge_set,
output_size,
num_filters=1,
max_depth=2,
act='tanh',
param_attr=None,
bias_attr=None,
name=None,
):
"""
${comment}
Args : nodes_vector(${nodes_vector_type}) : $ { nodes_vector_comment }
edge_set(${edge_set_type}) : $ { edge_set_comment }
output_size(int): output feature width
num_filters(int): number of filters, Default 1
max_depth(int): max depth of filters, Default 2
act(str): activation function, Default tanh
param_attr(ParamAttr): the parameter attribute for the filters, Default None
bias_attr(ParamAttr): the parameter attribute for the bias of this layer, Default None
name(str): a name of this layer(optional). If set None, the layer will be named automatically, Default None
Returns:
out(${out_type}): ${
out_comment
}
Examples:
.. code-block:: python
import paddle.fluid as fluid
# 10 for max_node_size of dataset, 5 for vector width
nodes_vector = fluid.layers.data(
name='vectors', shape=[10, 5], dtype='float32')
# 10 for max_node_size of dataset, 2 for every edge has two nodes
# edges must be directional
edge_set = fluid.layers.data(name='edge_set', shape=[
10, 2], dtype='float32')
# the shape of output will be [10, 6, 1],
# 10 for max_node_size of dataset, 6 for output size, 1 for 1 filter
out_vector = fluid.layers.tree_conv(nodes_vector, edge_set, 6, 1, 2)
#After reshape, output tensor could be nodes_vector for next tree convolution
out_vector = fluid.layers.reshape(out_vector, shape=[-1, 10, 6])
out_vector_2 = fluid.layers.tree_conv(out_vector, edge_set, 3, 4, 2)
#also output tensor could be pooling(the pooling in paper called global pooling)
pooled = fluid.layers.reduce_max(out_vector, dim=2) # global pooling
"""
check_type(nodes_vector, 'nodes_vector', (Variable), 'tree_conv')
check_type(edge_set, 'edge_set', (Variable), 'tree_conv')
helper = LayerHelper("tree_conv", **locals())
dtype = helper.input_dtype('nodes_vector')
feature_size = nodes_vector.shape[2]
W_shape = [feature_size, 3, output_size, num_filters]
W = helper.create_parameter(
attr=param_attr, shape=W_shape, dtype=dtype, is_bias=False
)
out = helper.create_variable_for_type_inference(dtype=dtype)
helper.append_op(
type='tree_conv',
inputs={'NodesVector': nodes_vector, 'EdgeSet': edge_set, 'Filter': W},
outputs={
'Out': out,
},
attrs={'max_depth': max_depth},
)
if helper.bias_attr:
pre_activation = helper.append_bias_op(out)
else:
pre_activation = out
return helper.append_activation(pre_activation)
def fused_embedding_seq_pool(
input,
size,
......
python/paddle/fluid/tests/unittests/sequence/test_sequence_topk_avg_pooling.py
浏览文件 @ da357615
......@@ -163,35 +163,6 @@ class TestSequenceTopkAvgPoolingOpCase1(TestSequenceTopkAvgPoolingOp):
col = [48]
self.init_data(topks, channel_num, row, col, dim)
def test_api(self):
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[1], lod_level=1)
row = fluid.layers.data(name='row', shape=[10], lod_level=1)
col = fluid.layers.data(name='col', shape=[10], lod_level=1)
topk_avg = fluid.contrib.sequence_topk_avg_pooling(
input=x, row=row, col=col, topks=[1, 3, 5], channel_num=5
)
place = fluid.CPUPlace()
x_tensor = fluid.create_lod_tensor(
np.random.rand(45, 1).astype('float32'), [[30, 15]], place
)
row_tensor = fluid.create_lod_tensor(
np.random.rand(5, 10).astype('float32'), [[2, 3]], place
)
col_tensor = fluid.create_lod_tensor(
np.random.rand(4, 10).astype('float32'), [[3, 1]], place
)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
ret = exe.run(
feed={'x': x_tensor, 'row': row_tensor, 'col': col_tensor},
fetch_list=[topk_avg],
return_numpy=False,
)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_imperative_load_static_param.py
浏览文件 @ da357615
......@@ -134,19 +134,6 @@ class TestDygraphLoadStatic(unittest.TestCase):
spe_norm_out_2 = paddle.static.nn.spectral_norm(weight=spec_norm, dim=1, power_iters=2)
'''
nodes_vector = fluid.data(
name='vectors', shape=[None, 10, 5], dtype='float32'
)
edge_set = fluid.data(
name='edge_set', shape=[None, 10, 2], dtype='float32'
)
tree_conv_out1 = fluid.contrib.layers.tree_conv(
nodes_vector, edge_set, 6, 1, 2
)
tree_conv_out2 = fluid.contrib.layers.tree_conv(
nodes_vector, edge_set, 6, 1, 2
)
para1 = paddle.create_parameter(
[100, 100], 'float32', name="weight_test_1"
)
......
python/paddle/fluid/tests/unittests/test_match_matrix_tensor_op.py
浏览文件 @ da357615
......@@ -17,8 +17,6 @@ import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid as fluid
class TestMatchMatrixTensorOp(OpTest):
def setUp(self):
......@@ -108,25 +106,6 @@ class TestMatchMatrixTensorOpCase4(TestMatchMatrixTensorOp):
y_lod = [[3, 2, 4, 1, 2]]
self.init_data(ix, x_lod, iy, y_lod, h, dim_t)
def test_api(self):
x_lod_tensor = fluid.layers.data(name='x', shape=[10], lod_level=1)
y_lod_tensor = fluid.layers.data(name='y', shape=[10], lod_level=1)
out, out_tmp = fluid.contrib.match_matrix_tensor(
x=x_lod_tensor, y=y_lod_tensor, channel_num=3
)
place = fluid.CPUPlace()
x_data = np.random.rand(7, 10).astype('float32')
y_data = np.random.rand(9, 10).astype('float32')
x = fluid.create_lod_tensor(x_data, [[2, 5]], place)
y = fluid.create_lod_tensor(y_data, [[3, 6]], place)
exe = fluid.Executor(place=place)
exe.run(fluid.default_startup_program())
ret = exe.run(
feed={'x': x, 'y': y}, fetch_list=[out], return_numpy=False
)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_tree_conv_op.py
浏览文件 @ da357615
......@@ -17,9 +17,6 @@ import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid as fluid
from paddle.fluid.framework import Program, program_guard
def collect_node_patch(og, max_depth):
"""
......@@ -166,35 +163,5 @@ class TestTreeConvOp(OpTest):
return vec
class TestTreeConv_OpError(unittest.TestCase):
def test_errors(self):
with program_guard(Program(), Program()):
nodes_vector_1 = np.random.random((10, 5)).astype("float32")
edge_set_1 = fluid.layers.data(
name='edge_set_1', shape=[10, 2], dtype='float32'
)
# the nodes_vector of tree_conv must be Variable.
self.assertRaises(
TypeError,
fluid.contrib.layers.tree_conv,
nodes_vector_1,
edge_set_1,
3,
)
nodes_vector_2 = fluid.layers.data(
name='vectors2', shape=[10, 5], dtype='float32'
)
edge_set_2 = np.random.random((10, 2)).astype("float32")
# the edge_set of tree_conv must be Variable.
self.assertRaises(
TypeError,
fluid.contrib.layers.tree_conv,
nodes_vector_2,
edge_set_2,
3,
)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_var_conv_2d.py
浏览文件 @ da357615
......@@ -295,42 +295,5 @@ class TestVarConv2DOpCase7(TestVarConv2DOp):
)
class TestVarConv2DApi(unittest.TestCase):
def test_api(self):
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[1], lod_level=1)
row = fluid.layers.data(name='row', shape=[6], lod_level=1)
col = fluid.layers.data(name='col', shape=[6], lod_level=1)
out = fluid.contrib.var_conv_2d(
input=x,
row=row,
col=col,
input_channel=3,
output_channel=5,
filter_size=[3, 3],
stride=1,
)
place = fluid.CPUPlace()
x_tensor = fluid.create_lod_tensor(
np.random.rand(116, 1).astype('float32'), [[60, 56]], place
)
row_tensor = fluid.create_lod_tensor(
np.random.rand(9, 6).astype('float32'), [[5, 4]], place
)
col_tensor = fluid.create_lod_tensor(
np.random.rand(13, 6).astype('float32'), [[6, 7]], place
)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
ret = exe.run(
feed={'x': x_tensor, 'row': row_tensor, 'col': col_tensor},
fetch_list=[out],
return_numpy=False,
)
if __name__ == '__main__':
unittest.main()
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