未验证 提交 479c861b 编写于 作者: C Cao Ying 提交者: GitHub

Merge pull request #7726 from lcy-seso/fix_rendering_error_of_transpose_op

fix rendering error of transpose operator and add wrapper.
......@@ -59,44 +59,39 @@ class TransposeOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"X",
"(Tensor)The input tensor, tensors with rank at most 6 are supported");
AddOutput("Out", "(Tensor)The output tensor");
"(Tensor) The input tensor, tensors with rank up to 6 are supported.");
AddOutput("Out", "(Tensor)The output tensor.");
AddAttr<std::vector<int>>(
"axis",
"(vector<int>)A list of values, and the size of the list should be "
"the same with the input tensor rank, the tensor will "
"permute the axes according the the values given");
"(vector<int>) A list of values, and the size of the list should be "
"the same with the input tensor rank. This operator permutes the input "
"tensor's axes according to the values given.");
AddComment(R"DOC(
Transpose Operator.
The input tensor will be permuted according to the axis values given.
The op functions is similar to how numpy.transpose works in python.
The input tensor will be permuted according to the axes given.
The behavior of this operator is similar to how `numpy.transpose` works.
For example:
- suppose the input `X` is a 2-D tensor:
$$
X = \begin{pmatrix}
0 &1 &2 \\
3 &4 &5
\end{pmatrix}$$
.. code-block:: text
the given `axes` is: $[1, 0]$, and $Y$ = transpose($X$, axis)
input = numpy.arange(6).reshape((2,3))
then the output $Y$ is:
the input is:
$$
Y = \begin{pmatrix}
0 &3 \\
1 &4 \\
2 &5
\end{pmatrix}$$
array([[0, 1, 2],
[3, 4, 5]])
given axis is:
[1, 0]
output = input.transpose(axis)
then the output is:
array([[0, 3],
[1, 4],
[2, 5]])
So, given a input tensor of shape(N, C, H, W) and the axis is {0, 2, 3, 1},
the output tensor shape will be (N, H, W, C)
- Given a input tensor with shape $(N, C, H, W)$ and the `axes` is
$[0, 2, 3, 1]$, then shape of the output tensor will be: $(N, H, W, C)$.
)DOC");
}
......
......@@ -171,8 +171,9 @@ def train(src_dict_size, trg_dict_size, src_lang="en"):
callable: The train reader.
"""
assert (src_lang in ["en", "de"], ("An error language type. Only support: "
"en (for English); de(for Germany)"))
if src_lang not in ["en", "de"]:
raise ValueError("An error language type. Only support: "
"en (for English); de(for Germany).")
src_dict_size, trg_dict_size = __get_dict_size(src_dict_size, trg_dict_size,
src_lang)
......@@ -218,9 +219,9 @@ def test(src_dict_size, trg_dict_size, src_lang="en"):
callable: The test reader.
"""
assert (src_lang in ["en", "de"],
("An error language type. "
"Only support: en (for English); de(for Germany)"))
if src_lang not in ["en", "de"]:
raise ValueError("An error language type. "
"Only support: en (for English); de(for Germany).")
src_dict_size, trg_dict_size = __get_dict_size(src_dict_size, trg_dict_size,
src_lang)
......@@ -266,9 +267,9 @@ def validation(src_dict_size, trg_dict_size, src_lang="en"):
Returns:
callable: The validation reader.
"""
assert (src_lang in ["en", "de"],
("An error language type. "
"Only support: en (for English); de(for Germany)"))
if src_lang not in ["en", "de"]:
raise ValueError("An error language type. "
"Only support: en (for English); de(for Germany).")
src_dict_size, trg_dict_size = __get_dict_size(src_dict_size, trg_dict_size,
src_lang)
......
......@@ -22,14 +22,41 @@ from ..param_attr import ParamAttr
from tensor import concat
__all__ = [
'fc', 'embedding', 'dynamic_lstm', 'gru_unit', 'linear_chain_crf',
'crf_decoding', 'cos_sim', 'cross_entropy', 'square_error_cost', 'accuracy',
'chunk_eval', 'sequence_conv', 'conv2d', 'sequence_pool', 'pool2d',
'batch_norm', 'beam_search_decode', 'conv2d_transpose', 'sequence_expand',
'lstm_unit', 'reduce_sum', 'reduce_mean', 'reduce_max', 'reduce_min',
'sequence_first_step', 'sequence_last_step', 'dropout', 'split',
'ctc_greedy_decoder', 'edit_distance', 'l2_normalize', 'matmul', 'warpctc',
'sequence_reshape'
'fc',
'embedding',
'dynamic_lstm',
'gru_unit',
'linear_chain_crf',
'crf_decoding',
'cos_sim',
'cross_entropy',
'square_error_cost',
'accuracy',
'chunk_eval',
'sequence_conv',
'conv2d',
'sequence_pool',
'pool2d',
'batch_norm',
'beam_search_decode',
'conv2d_transpose',
'sequence_expand',
'lstm_unit',
'reduce_sum',
'reduce_mean',
'reduce_max',
'reduce_min',
'sequence_first_step',
'sequence_last_step',
'dropout',
'split',
'ctc_greedy_decoder',
'edit_distance',
'l2_normalize',
'matmul',
'warpctc',
'sequence_reshape',
'transpose',
]
......@@ -44,14 +71,14 @@ def fc(input,
**Fully Connected Layer**
The fully connected layer can take multiple tensors as its inputs. It
creates a variable (one for each input tensor) called weights for each input
tensor, which represents a fully connected weight matrix from each input
unit to each output unit. The fully connected layer multiplies each input
tensor with its coresponding weight to produce an output Tensor. If
multiple input tensors are given, the results of multiple multiplications
will be sumed up. If bias_attr is not None, a biases variable will be
created and added to the output. Finally, if activation is not None,
it will be applied to the output as well.
creates a variable (one for each input tensor) called weights for each
input tensor, which represents a fully connected weight matrix from
each input unit to each output unit. The fully connected layer
multiplies each input tensor with its coresponding weight to produce
an output Tensor. If multiple input tensors are given, the results of
multiple multiplications will be sumed up. If bias_attr is not None,
a biases variable will be created and added to the output. Finally,
if activation is not None, it will be applied to the output as well.
This process can be formulated as follows:
......@@ -1814,11 +1841,11 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
- If both are 2-D, they are multiplied like conventional matrices.
- If either is n-D, it is treated as a stack of matrices residing in the
last two dimensions and a batched matrix multiply supporting broadcast
last two dimensions and a batched matrix multiply supporting broadcast
applies on the two tensors.
Also note that if the raw tensor :math:`x` or :math:`y` is rank-1 and
nontransposed, the prepended or appended dimension :math:`1` will be
Also note that if the raw tensor :math:`x` or :math:`y` is rank-1 and
nontransposed, the prepended or appended dimension :math:`1` will be
removed after matrix multiplication.
Args:
......@@ -2112,3 +2139,41 @@ def sequence_reshape(input, new_dim):
outputs={'Out': [out]},
attrs={'new_dim': new_dim})
return out
def transpose(x, perm, name=None):
"""
**transpose Layer**
Permute the dimensions of `input` according to `perm`.
The `i`-th dimension of the returned tensor will correspond to the
perm[i]-th dimension of `input`.
Args:
input (Variable): (Tensor), A Tensor.
perm (list): A permutation of the dimensions of `input`.
Returns:
Variable: A transposed Tensor.
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[5, 10, 15], dtype='float32')
x_transposed = layers.transpose(x, perm=[1, 0, 2])
"""
if len(perm) != len(x.shape):
raise ValueError(
"Input(perm) is the permutation of dimensions of Input(input). "
"It's length shoud be equal to Input(input)'s rank.")
helper = LayerHelper('transpose', **locals())
out = helper.create_tmp_variable(x.dtype)
helper.append_op(
type='transpose',
inputs={'X': [x]},
outputs={'Out': [out]},
attrs={'axis': perm})
return out
......@@ -45,10 +45,20 @@ __activations__ = [
]
__all__ = [
'mean', 'mul', 'reshape', 'scale', 'transpose',
'sigmoid_cross_entropy_with_logits', 'elementwise_add', 'elementwise_div',
'elementwise_sub', 'elementwise_mul', 'elementwise_max', 'elementwise_min',
'clip', 'clip_by_norm', 'sequence_softmax'
'mean',
'mul',
'reshape',
'scale',
'sigmoid_cross_entropy_with_logits',
'elementwise_add',
'elementwise_div',
'elementwise_sub',
'elementwise_mul',
'elementwise_max',
'elementwise_min',
'clip',
'clip_by_norm',
'sequence_softmax',
] + __activations__
for _OP in set(__all__):
......
......@@ -65,13 +65,13 @@ def lstm_net(dict_dim, class_dim=2, emb_dim=32, seq_len=80, batch_size=50):
emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim])
emb = fluid.layers.reshape(x=emb, shape=[batch_size, seq_len, emb_dim])
emb = fluid.layers.transpose(x=emb, axis=[1, 0, 2])
emb = fluid.layers.transpose(x=emb, perm=[1, 0, 2])
c_pre_init = fluid.layers.fill_constant(
dtype=emb.dtype, shape=[batch_size, emb_dim], value=0.0)
c_pre_init.stop_gradient = False
layer_1_out = lstm(emb, c_pre_init=c_pre_init, hidden_dim=emb_dim)
layer_1_out = fluid.layers.transpose(x=layer_1_out, axis=[1, 0, 2])
layer_1_out = fluid.layers.transpose(x=layer_1_out, perm=[1, 0, 2])
prediction = fluid.layers.fc(input=layer_1_out,
size=class_dim,
......
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