remove test_seq_concat_op

paddle/fluid/API.spec

@@ -174,6 +174,7 @@ paddle.fluid.layers.stack ArgSpec(args=['x', 'axis'], varargs=None, keywords=Non
 paddle.fluid.layers.pad2d ArgSpec(args=['input', 'paddings', 'mode', 'pad_value', 'data_format', 'name'], varargs=None, keywords=None, defaults=([0, 0, 0, 0], 'constant', 0.0, 'NCHW', None))
 paddle.fluid.layers.unstack ArgSpec(args=['x', 'axis', 'num'], varargs=None, keywords=None, defaults=(0, None))
 paddle.fluid.layers.sequence_enumerate ArgSpec(args=['input', 'win_size', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0, None))
+paddle.fluid.layers.sequence_concat ArgSpec(args=['input', '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))

paddle/fluid/operators/sequence_concat_op.cc

@@ -37,13 +37,12 @@ class SeqConcatOpMaker : public framework::OpProtoAndCheckerMaker {
 class SeqConcatShapeInferer : public framework::InferShapeBase {
  public:
   void operator()(framework::InferShapeContext *context) const override {
-    try {
     PADDLE_ENFORCE(context->HasInputs("X"),
                    "Input(X) of Sequence Concat Op should not be null.");
     PADDLE_ENFORCE(context->HasOutput("Out"),
                    "Output(Out) of Sequence Concat Op should not be null.");
 
-      PADDLE_ENFORCE_GT(context->HasInputs("X"), 1,
+    PADDLE_ENFORCE_GT(context->Inputs("X").size(), 1,
                       "The number of input sequences is at least two.");
     auto x_dims = context->GetInputsDim("X");
     int64_t batch_size = 0;
@@ -71,9 +70,6 @@ class SeqConcatShapeInferer : public framework::InferShapeBase {
       // in Kernel.
       context->ShareLoD("X", "Out");
     }
-    } catch (...) {
-      PADDLE_THROW("Unknown error");
-    }
   }
 };
 

python/paddle/fluid/tests/unittests/test_seq_concat_op.py

-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import print_function
-
-import unittest
-import numpy as np
-import sys
-from op_test import OpTest
-
-
-def to_abs_offset_lod(lod):
-    offset_lod = [[0] for i in lod]
-    for i, level in enumerate(lod):
-        for seq_len in level:
-            offset_lod[i].append(offset_lod[i][-1] + seq_len)
-
-    if len(offset_lod) == 0 or len(offset_lod) == 1:
-        return offset_lod
-    import copy
-    new_offset_lod = copy.deepcopy(offset_lod)
-    for idx, val in enumerate(offset_lod[0]):
-        new_offset_lod[0][idx] = offset_lod[1][val]
-    return new_offset_lod
-
-
-def seq_concat(inputs, level):
-    lod0 = inputs['X'][0][1][1]
-    lod1 = inputs['X'][1][1][1]
-    x0 = inputs['X'][0][1][0]
-    x1 = inputs['X'][1][1][0]
-    level_idx = len(lod0) - level - 1
-    outs = []
-    for i in range(len(lod0[level_idx])):
-        sub_x0 = x0[to_abs_offset_lod(lod0)[level_idx][i]:to_abs_offset_lod(
-            lod0)[level_idx][i + 1], :]
-        sub_x1 = x1[to_abs_offset_lod(lod1)[level_idx][i]:to_abs_offset_lod(
-            lod1)[level_idx][i + 1], :]
-        outs.append(np.concatenate((sub_x0, sub_x1), axis=0))
-    return np.concatenate(outs, axis=0)
-
-
-class TestSeqConcatOp(OpTest):
-    def set_data(self):
-        # two level, batch size is 3
-        x0 = np.random.random((4, 6, 3)).astype('float32')
-        lod0 = [[2, 2], [1, 1, 1, 1]]
-        x1 = np.random.random((4, 8, 3)).astype('float32')
-        lod1 = [[2, 2], [1, 1, 1, 1]]
-        axis = 1
-        level = 1
-        self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
-        self.attrs = {'axis': axis, 'level': level}
-        self.outputs = {'Out': (np.concatenate([x0, x1], axis=1), lod0)}
-
-    def setUp(self):
-        self.op_type = "sequence_concat"
-        self.set_data()
-
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad(self):
-        self.check_grad(['x0'], 'Out')
-
-
-class TestSeqConcatOpLevelZeroNestedSequence(TestSeqConcatOp):
-    def set_data(self):
-        # two level, batch size is 3
-        x0 = np.random.random((4, 6, 3)).astype('float32')
-        lod0 = [[2, 2], [1, 1, 1, 1]]
-        x1 = np.random.random((7, 6, 3)).astype('float32')
-        lod1 = [[2, 2], [1, 2, 2, 2]]
-        axis = 0
-        level = 0
-        self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
-        self.attrs = {'axis': axis, 'level': level}
-        out_lod = [[2, 2], [2, 3, 3, 3]]
-        self.outputs = {'Out': (seq_concat(self.inputs, level), out_lod)}
-
-
-class TestSeqConcatOplevelOneNestedSequence(TestSeqConcatOp):
-    def set_data(self):
-        # two level, batch size is 3
-        x0 = np.random.random((4, 6, 3)).astype('float32')
-        lod0 = [[2, 2], [1, 1, 1, 1]]
-        x1 = np.random.random((7, 6, 3)).astype('float32')
-        lod1 = [[3, 1], [1, 2, 2, 2]]
-        axis = 0
-        level = 1
-        self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
-        self.attrs = {'axis': axis, 'level': level}
-        out_lod = [[5, 3], [1, 1, 1, 2, 2, 1, 1, 2]]
-        self.outputs = {'Out': (seq_concat(self.inputs, level), out_lod)}
-
-
-class TestSeqConcatOpLevelZeroSequence(TestSeqConcatOp):
-    def set_data(self):
-        # two level, batch size is 3
-        x0 = np.random.random((4, 3, 4)).astype('float32')
-        lod0 = [[1, 1, 1, 1]]
-        x1 = np.random.random((7, 3, 4)).astype('float32')
-        lod1 = [[1, 2, 2, 2]]
-        axis = 0
-        level = 0
-        self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
-        self.attrs = {'axis': axis, 'level': level}
-        out_lod = [[2, 3, 3, 3]]
-        self.outputs = {'Out': (seq_concat(self.inputs, level), out_lod)}
-
-
-if __name__ == '__main__':
-    unittest.main()