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feat: wrap sequence enumerate op

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fix-deadlinks-in-readme
chenweihang 7 years ago
parent d2e5395b97
commit 219a2369da
6 changed files with 85 additions and 85 deletions

1
paddle/fluid/API.spec
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@ -161,6 +161,7 @@ paddle.fluid.layers.crop ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs
paddle.fluid.layers.rank_loss ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.prelu ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.flatten ArgSpec(args=['x', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None))
paddle.fluid.layers.sequence_enumerate ArgSpec(args=['input', 'win_size', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.open_recordio_file ArgSpec(args=['filename', 'shapes', 'lod_levels', 'dtypes', 'pass_num', 'for_parallel'], varargs=None, keywords=None, defaults=(1, True))
paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))

20
paddle/fluid/operators/sequence_enumerate_op.cc
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@ -13,6 +13,7 @@
// limitations under the License.
#include "paddle/fluid/operators/sequence_enumerate_op.h"
#include <vector>
namespace paddle {
namespace operators {
@ -30,16 +31,21 @@ class SequenceEnumerateOp : public framework::OperatorWithKernel {
"Output(X) of SequenceEnumerate operator should not be null.");
const auto x_dims = ctx->GetInputDim("X");
PADDLE_ENFORCE(
x_dims.size() == 2 && x_dims[1] == 1,
"Input(X) of SequenceEnumerate operator should be a 2-D LoDTensor "
"with the 2nd dimension equal to 1.");
PADDLE_ENFORCE_EQ(
x_dims.size(), 2UL,
"Input(X) of SequenceEnumerate operator's rank should be 2.");
const auto win_size = ctx->Attrs().Get<int>("win_size");
PADDLE_ENFORCE(win_size <= x_dims[0],
// TODO(chenweihang): unittest doesn't has batch size, but test_layers has
auto first_dim = x_dims[0] == -1 ? x_dims[1] : x_dims[0];
PADDLE_ENFORCE(win_size <= first_dim,
"The enumerate window size should be less than or equal to "
"input sequence length.");
ctx->SetOutputDim("Out", {x_dims[0], win_size});
std::vector<int64_t> out_shape(x_dims.size() + 1, 0);
for (int i = 0; i < x_dims.size(); ++i) out_shape.emplace_back(x_dims[i]);
out_shape.emplace_back(win_size);
ctx->SetOutputDim("Out", framework::make_ddim(out_shape));
ctx->ShareLoD("X", "Out");
}
};
@ -83,8 +89,6 @@ Case 1:
Out.data = [[1, 2], [2, 3], [3, 4], [4, 5], [0, 0]]
Out.dims = [5, 2]
Currently, only 1-level LoDTensor is supported.
)DOC");
}
};

2
paddle/fluid/operators/sequence_enumerate_op.cu
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@ -48,8 +48,6 @@ class SequenceEnumerateOpCUDAKernel : public framework::OpKernel<T> {
auto in_dims = in->dims();
auto in_lod = in->lod();
PADDLE_ENFORCE_EQ(in_lod.size(), 1UL,
"Only support one level sequence now.");
PADDLE_ENFORCE_EQ(
static_cast<uint64_t>(in_dims[0]), in_lod[0].back(),
"The actual input data's size mismatched with LoD information.");

2
paddle/fluid/operators/sequence_enumerate_op.h
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@ -32,8 +32,6 @@ class SequenceEnumerateKernel : public framework::OpKernel<T> {
auto in_dims = in->dims();
auto in_lod = in->lod();
PADDLE_ENFORCE_EQ(in_lod.size(), 1UL,
"Only support one level sequence now.");
PADDLE_ENFORCE_EQ(
static_cast<uint64_t>(in_dims[0]), in_lod[0].back(),
"The actual input data's size mismatched with LoD information.");

136
python/paddle/fluid/layers/nn.py
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@ -29,79 +29,22 @@ from .. import unique_name
from functools import reduce
__all__ = [
'fc',
'embedding',
'dynamic_lstm',
'dynamic_lstmp',
'dynamic_gru',
'gru_unit',
'linear_chain_crf',
'crf_decoding',
'cos_sim',
'cross_entropy',
'square_error_cost',
'chunk_eval',
'sequence_conv',
'conv2d',
'conv3d',
'sequence_pool',
'sequence_softmax',
'softmax',
'pool2d',
'pool3d',
'batch_norm',
'beam_search_decode',
'conv2d_transpose',
'conv3d_transpose',
'sequence_expand',
'lstm_unit',
'reduce_sum',
'reduce_mean',
'reduce_max',
'reduce_min',
'reduce_prod',
'sequence_first_step',
'sequence_last_step',
'dropout',
'split',
'ctc_greedy_decoder',
'edit_distance',
'l2_normalize',
'matmul',
'topk',
'warpctc',
'sequence_reshape',
'transpose',
'im2sequence',
'nce',
'hsigmoid',
'beam_search',
'row_conv',
'multiplex',
'layer_norm',
'softmax_with_cross_entropy',
'smooth_l1',
'one_hot',
'autoincreased_step_counter',
'reshape',
'lod_reset',
'lrn',
'pad',
'label_smooth',
'roi_pool',
'dice_loss',
'image_resize',
'image_resize_short',
'resize_bilinear',
'gather',
'random_crop',
'mean_iou',
'relu',
'log',
'crop',
'rank_loss',
'prelu',
'flatten',
'fc', 'embedding', 'dynamic_lstm', 'dynamic_lstmp', 'dynamic_gru',
'gru_unit', 'linear_chain_crf', 'crf_decoding', 'cos_sim', 'cross_entropy',
'square_error_cost', 'chunk_eval', 'sequence_conv', 'conv2d', 'conv3d',
'sequence_pool', 'sequence_softmax', 'softmax', 'pool2d', 'pool3d',
'batch_norm', 'beam_search_decode', 'conv2d_transpose', 'conv3d_transpose',
'sequence_expand', 'lstm_unit', 'reduce_sum', 'reduce_mean', 'reduce_max',
'reduce_min', 'reduce_prod', 'sequence_first_step', 'sequence_last_step',
'dropout', 'split', 'ctc_greedy_decoder', 'edit_distance', 'l2_normalize',
'matmul', 'topk', 'warpctc', 'sequence_reshape', 'transpose', 'im2sequence',
'nce', 'hsigmoid', 'beam_search', 'row_conv', 'multiplex', 'layer_norm',
'softmax_with_cross_entropy', 'smooth_l1', 'one_hot',
'autoincreased_step_counter', 'reshape', 'lod_reset', 'lrn', 'pad',
'label_smooth', 'roi_pool', 'dice_loss', 'image_resize',
'image_resize_short', 'resize_bilinear', 'gather', 'random_crop',
'mean_iou', 'relu', 'log', 'crop', 'rank_loss', 'prelu', 'flatten',
'sequence_enumerate'
]
@ -5475,3 +5418,50 @@ def flatten(x, axis=1, name=None):
outputs={'Out': out},
attrs={"axis": axis})
return out
def sequence_enumerate(input, win_size, pad_value, name=None):
"""
Generate a new LoDTensor
with the same 1st dimension length as the original LoDTensor,
and with the 2nd dimension equal to the input window length,
the new sub-sequence on 2nd dimension is enumerated one by one on the original sequence.
The values of the last insufficient part areall filled with the input pad_value.
Examples:
Case 1:
Input:
X.lod = [[0, 3, 5]]
X.data = [1, 2, 3, 4, 5]
X.dims = [5, 1]
Attrs:
win_size = 2
pad_value = 0
Output:
Out.lod = [[0, 3, 5]]
Out.data = [[1, 2], [2, 3], [3, 4], [4, 5], [0, 0]]
Out.dims = [5, 2]
Args:
input (Variable): The input variable which is a LoDTensor
win_size (int): The enumerate sequence window size.
pad_value (int): The enumerate sequence padding value.
Returns:
Variable: The enumerate sequence variable which is a LoDTensor.
Examples:
.. code-block:: python
x = fluid.layers.data(shape[30, 1], dtype='int32', lod_level=1)
out = fluid.layers.sequence_enumerate(input=x, win_size=3, pad_value=0)
"""
helper = LayerHelper('sequence_enumerate', **locals())
out = helper.create_tmp_variable(helper.input_dtype())
helper.append_op(
type='sequence_enumerate',
inputs={'X': input},
outputs={'Out': out},
attrs={'win_size': win_size,
'pad_value': pad_value})
return out

9
python/paddle/fluid/tests/unittests/test_layers.py
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@ -500,6 +500,15 @@ class TestBook(unittest.TestCase):
self.assertIsNotNone(out)
print(str(program))
def test_sequence_enumerate(self):
program = Program()
with program_guard(program):
x = layers.data(
name="input", shape=[30], dtype='int32', lod_level=1)
out = layers.sequence_enumerate(input=x, win_size=2, pad_value=0)
self.assertIsNotNone(out)
print(str(program))
if __name__ == '__main__':
unittest.main()

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