fengjiayi
7 years ago
parent
3c749fae43
commit
8d8d48a34f
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import OpTest
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class TestSequencePadOp(OpTest):
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def set_attr(self):
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self.x_shape = [12, 4]
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self.x_len_lod = [[2, 3, 4, 3]]
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self.pad_value = [1.0]
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self.padded_length = -1
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self.dtype = 'float32'
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def set_data(self):
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x_data = np.random.uniform(0.1, 0.5, self.x_shape).astype(self.dtype)
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pad_value_data = np.array(self.pad_value).astype(self.dtype)
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self.inputs = {
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'X': (x_data, self.x_len_lod),
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'PadValue': pad_value_data
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}
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self.attrs = {'padded_length': self.padded_length}
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def compute(self):
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# get padded length
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padded_length = self.padded_length
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x_len_lod_0 = self.x_len_lod[0]
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if padded_length == -1:
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max_seq_len = 0
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for l in x_len_lod_0:
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max_seq_len = max(max_seq_len, l)
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padded_length = max_seq_len
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# do padding
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x_data = self.inputs['X'][0]
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pad_value_data = self.inputs['PadValue']
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if pad_value_data.shape == (1, ):
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pad_value_data = np.broadcast_to(
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pad_value_data, shape=x_data.shape[1:])
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padded_sequences = []
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start_idx = 0
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for l in x_len_lod_0:
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end_idx = start_idx + l
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seq = x_data[start_idx:end_idx]
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to_pad_len = padded_length - l
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for _ in range(to_pad_len):
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seq = np.append(seq, pad_value_data[np.newaxis, :], axis=0)
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padded_sequences.append(seq)
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start_idx = end_idx
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out_len_lod = self.x_len_lod[:]
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out_len_lod_0 = [padded_length] * len(x_len_lod_0)
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out_len_lod[0] = out_len_lod_0
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out_data = np.concatenate(padded_sequences, axis=0)
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self.outputs = {'Out': (out_data, out_len_lod)}
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def setUp(self):
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self.op_type = 'sequence_pad'
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self.set_attr()
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self.set_data()
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self.compute()
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(["X"], "Out")
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class TestSequencePadOp2(TestSequencePadOp):
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def set_attr(self):
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self.x_shape = [12, 4]
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self.x_len_lod = [[2, 3, 4, 3]]
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self.pad_value = [1.0, 2.0, 3.0, 4.0]
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self.padded_length = -1
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self.dtype = 'float32'
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class TestSequencePadOp3(TestSequencePadOp):
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def set_attr(self):
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self.x_shape = [12, 4]
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self.x_len_lod = [[2, 3, 4, 3]]
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self.pad_value = [1.0]
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self.padded_length = 7
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self.dtype = 'float32'
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class TestSequencePadOp4(TestSequencePadOp):
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def set_attr(self):
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self.x_shape = [12, 4]
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self.x_len_lod = [[2, 3, 4, 3]]
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self.pad_value = [1.0, 2.0, 3.0, 4.0]
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self.padded_length = 7
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self.dtype = 'float32'
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class TestSequencePadOp5(TestSequencePadOp):
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def set_attr(self):
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self.x_shape = [12, 2, 2]
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self.x_len_lod = [[2, 3, 4, 3]]
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self.pad_value = [1.0]
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self.padded_length = -1
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self.dtype = 'float32'
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class TestSequencePadOp6(TestSequencePadOp):
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def set_attr(self):
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self.x_shape = [12, 2, 2]
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self.x_len_lod = [[2, 3, 4, 3]]
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self.pad_value = [[1.0, 2.0], [3.0, 4.0]]
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self.padded_length = -1
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self.dtype = 'float32'
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class TestSequencePadOp7(TestSequencePadOp):
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def set_attr(self):
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self.x_shape = [12, 2, 2]
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self.x_len_lod = [[2, 3, 4, 3]]
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self.pad_value = [1.0]
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self.padded_length = 7
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self.dtype = 'float32'
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