Merge branch 'release/v0.2'

docker/Dockerfile

@@ -8,9 +8,7 @@ RUN pip install cython
 RUN pip install --upgrade pip
 RUN pip install -r requirements.txt
 
-RUN pip install cmake
 RUN mkdir build
-
 WORKDIR /workspace/khaiii/build
 
 RUN cmake ..

include/khaiii/khaiii_api.h

@@ -12,7 +12,7 @@
 // constants //
 ///////////////
 #define KHAIII_VERSION_MAJOR 0
-#define KHAIII_VERSION_MINOR 1
+#define KHAIII_VERSION_MINOR 2
 #define _MAC2STR(m) #m
 #define _JOIN_VER(x,y) _MAC2STR(x) "." _MAC2STR(y)    // NOLINT
 #define KHAIII_VERSION _JOIN_VER(KHAIII_VERSION_MAJOR,KHAIII_VERSION_MINOR)    // NOLINT

requirements.txt

@@ -1,3 +1 @@
 cmake>=3.10
-numpy
-tqdm

rsc/lib/vocabulary.py

@@ -11,22 +11,16 @@
 ###########
 # imports #
 ###########
-import re
 import codecs
-from collections import defaultdict
 import copy
 import logging
 import os
-import torch
-from torch import nn
-import numpy as np
-from tqdm import tqdm
 
 
 #########
 # types #
 #########
-class Vocabulary(object):
+class Vocabulary:
     """
     vocabulary class
     """
@@ -69,6 +63,9 @@ def __getitem__(self, key):
     def __len__(self):
         return len(self.dic)
 
+    '''
+    # 리소스 빌드 시 pytorch 의존성 제거를 위해 임시로 메서드를 제거합니다.
+    # 추후 학습 코드를 추가할 때 이 부분을 리팩토링 합니다.
     def get_embedding(self, dim, padding_idx=None):
         """
         embedding을 리턴합니다.
@@ -79,6 +76,7 @@ def get_embedding(self, dim, padding_idx=None):
         if padding_idx:
             return nn.Embedding(len(self), dim, padding_idx=padding_idx)
         return nn.Embedding(len(self), dim)
+    '''    # pylint: disable=pointless-string-statement
 
     def padding_idx(self):
         """
@@ -117,65 +115,3 @@ def _load(self, path, cutoff=1):
             self.rev.append(entry)
             append_num += 1
         logging.info('%s: %d entries, %d cutoff', os.path.basename(path), append_num, cutoff_num)
-
-
-class PreTrainedVocabulary(Vocabulary):
-    """
-    pre-train된 word2vec를 사용하는 경우, vector에 있는 어휘로
-    사전을 구성하도록 합니다.
-    """
-    def __init__(self, path): #pylint: disable=super-init-not-called
-        """
-        Args:
-            path: file path
-        """
-        # simple : 사과/N , none : 사과
-        # 읽어들인 glove의 키 타입을 보고 판단해놓는다.
-        self.glove_key_type = None
-        self.dic, self.vectors = self._load_glove(path)
-        self.rev = {val:key for key, val in self.dic.items()}
-        assert len(self.dic) == len(self.rev)
-        logging.info('%s: %d entries, %d dim - not trainable',
-                     os.path.basename(path), len(self.dic), self.vectors.size(1))
-
-    def get_embedding(self, dim, padding_idx=None):
-        """
-        pre-training된 벡터가 세팅된 embedding을 리턴합니다.
-        """
-        assert dim == self.vectors.size(1)
-        embed = super().get_embedding(dim, padding_idx)
-        embed.weight = nn.Parameter(self.vectors, requires_grad=False)
-        return embed
-
-    def _load_glove(self, path):
-        """
-        pre-trained GloVe (텍스트 포맷) 워드 벡터를 읽어들인다.
-        Args:
-            path:  워드 벡터 경로
-        """
-        unk = None
-        vecs = []
-        for line in tqdm(codecs.open(path, 'r', encoding='UTF-8')):
-            cols = line.split(' ')
-            word = cols[0]
-            vec = np.array([float(_) for _ in cols[1:]])
-            if vec.size == 0: # format error
-                continue
-            if word == '<unk>':
-                unk = vec
-                continue
-            vecs.append((word, vec))
-            if self.glove_key_type is None:
-                if re.search('/[A-Z]$', word) is None:
-                    self.glove_key_type = 'none'
-                else:
-                    self.glove_key_type = 'simple'
-        if unk is None:
-            unk = [0] * len(vecs[0][1])
-        padding = [0] * len(vecs[0][1])
-        vecs.sort(key=lambda x: x[0])
-        vecs.insert(0, ('<unk>', unk))
-        vecs.insert(1, ('<p>', padding))
-        vocab = defaultdict(int)
-        vocab.update({word: idx for idx, (word, _) in enumerate(vecs)})
-        return vocab, torch.Tensor([vec for _, vec in vecs])

src/main/python/setup.py.in

@@ -89,7 +89,7 @@ setup(
     packages=['khaiii', ],
     include_package_data=True,
     install_requires=[],
-    setup_requires=['numpy', 'pytest-runner', 'tqdm'],
+    setup_requires=['cmake>=3.10', 'pytest-runner'],
     tests_require=['pytest', ],
     zip_safe=False,
     cmdclass={'build': CustomBuild}