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155 lines
4.5 KiB
155 lines
4.5 KiB
# Copyright (c) 2016 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 sys
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sys.path.append("../")
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from paddle.trainer.PyDataProviderWrapper import *
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import random
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import json
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import string
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@provider(slots=[
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SparseNonValueSlot(10), DenseSlot(2), SparseValueSlot(10), StringSlot(1),
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IndexSlot(3)
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])
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def processNonSequenceData(obj, filename):
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with open(filename, "rb") as f:
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for line in f:
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slots_str = line.split(';')
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index = int(slots_str[0])
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non_values = map(int, slots_str[1].split()[1:])
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dense = map(float, slots_str[2].split()[1:])
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strs = slots_str[4].strip().split(' ', 1)[1]
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def __values_mapper__(s):
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s = s.split(":")
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return int(s[0]), float(s[1])
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values = map(__values_mapper__, slots_str[3].split()[1:])
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yield [non_values, dense, values, strs, index]
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SPARSE_ID_LIMIT = 1000
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SPARSE_ID_COUNT = 100
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SEQUENCE_LIMIT = 50
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STRING_LIMIT = 10
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sparse_id_randomer = lambda: random.randrange(0, SPARSE_ID_LIMIT - 1)
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sparse_count_randomer = lambda: random.randrange(1, SPARSE_ID_COUNT)
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val_randomer = lambda: random.uniform(-1.0, 1.0)
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seq_count_randomer = lambda: random.randrange(1, SEQUENCE_LIMIT)
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str_count_randomer = lambda: random.randrange(1, STRING_LIMIT)
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class IDRandomer(): # A random generator, return unique id
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def __init__(self):
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self.id_set = set()
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def __call__(self):
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idx = sparse_id_randomer()
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if idx not in self.id_set:
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self.id_set.add(idx)
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return idx
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else:
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return self.__call__()
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# SparseValueSlot
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def sparse_value_creator(_):
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rand = IDRandomer()
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return [(rand(), val_randomer()) for _ in xrange(sparse_count_randomer())]
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sparse_value = map(sparse_value_creator, range(seq_count_randomer()))
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# DenseSlot
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def dense_creator(_):
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return [val_randomer() for _ in xrange(SPARSE_ID_LIMIT)]
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dense = map(dense_creator, range(seq_count_randomer()))
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# SparseNonValueSlot
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def sparse_creator(_):
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rand = IDRandomer()
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return [rand() for _ in xrange(sparse_count_randomer())]
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sparse_nonvalue = map(sparse_creator, range(seq_count_randomer()))
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# IndexSlot
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ids = [sparse_id_randomer() for _ in range(seq_count_randomer())]
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# StringSlot
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def random_str(size=8, chars=string.ascii_letters + string.digits):
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return ''.join(random.choice(chars) for _ in range(size))
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strs = [random_str(str_count_randomer()) for _ in range(seq_count_randomer())]
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def processSeqAndGenerateDataInit(obj, *args, **kwargs):
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obj.json_filename = kwargs.get("load_data_args", "test_data.json")
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@provider(
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slots=[
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SparseValueSlot(SPARSE_ID_LIMIT), DenseSlot(SPARSE_ID_LIMIT),
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SparseNonValueSlot(SPARSE_ID_LIMIT), IndexSlot(SPARSE_ID_LIMIT),
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StringSlot(SPARSE_ID_LIMIT)
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],
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use_seq=True,
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init_hook=processSeqAndGenerateDataInit)
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def processSeqAndGenerateData(obj, name):
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retv = [sparse_value, dense, sparse_nonvalue, ids, strs]
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# Write to protoseq.
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with open(obj.json_filename, "w") as f:
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json.dump(retv, f)
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yield retv
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def processSubSeqAndGenerateDataInit(obj, *args, **kwargs):
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obj.json_filename = kwargs.get("load_data_args", "test_data.json")
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@provider(
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slots=[
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SparseValueSlot(SPARSE_ID_LIMIT), DenseSlot(SPARSE_ID_LIMIT),
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SparseNonValueSlot(SPARSE_ID_LIMIT), IndexSlot(SPARSE_ID_LIMIT),
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StringSlot(SPARSE_ID_LIMIT)
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],
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use_seq=True,
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init_hook=processSubSeqAndGenerateDataInit)
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def processSubSeqAndGenerateData(obj, name):
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retv_json = [sparse_value, dense, sparse_nonvalue, ids, strs]
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retv_wrapper = [[sparse_value], [dense], [sparse_nonvalue], [ids], [strs]]
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# Write to protoseq.
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with open(obj.json_filename, "w") as f:
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json.dump(retv_json, f)
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yield retv_wrapper
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if __name__ == "__main__":
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pvd = processNonSequenceData("test.txt")
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print pvd.getNextBatch(100)
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pvd = processSeqAndGenerateData("_")
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print pvd.getNextBatch(100)
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pvd = processSubSeqAndGenerateData("_")
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print pvd.getNextBatch(1)
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