def __init__(self, model_proto, is_training=False):
"""Initializes the model.
Args:
model_proto: an instance of text_classification_model_pb2.TextClassificationModel
is_training: if True, training graph will be built.
"""
super(Model, self).__init__(model_proto, is_training)
if not isinstance(
model_proto,
text_classification_model_pb2.TextClassificationModel):
raise ValueError(
'The model_proto has to be an instance of TextClassificationModel.'
)
options = model_proto
self._open_vocabulary_list = model_utils.read_vocabulary(
options.open_vocabulary_file)
with open(options.open_vocabulary_glove_file, 'rb') as fid:
self._open_vocabulary_initial_embedding = np.load(fid)
self._vocabulary_list = model_utils.read_vocabulary(
options.vocabulary_file)
self._num_classes = len(self._vocabulary_list)
def __init__(self, model_proto, is_training=False):
"""Initializes the model.
Args:
model_proto: an instance of nod_model_pb2.NODModel
is_training: if True, training graph will be built.
"""
super(Model, self).__init__(model_proto, is_training)
if not isinstance(model_proto, nod_model_pb2.NODModel):
raise ValueError(
'The model_proto has to be an instance of NODModel.')
options = model_proto
self._vocabulary_list = model_utils.read_vocabulary(
options.vocabulary_file)
self._num_classes = len(self._vocabulary_list)
self._feature_extractor = build_faster_rcnn_feature_extractor(
options.feature_extractor, is_training,
options.inplace_batchnorm_update)
self._midn_post_process_fn = function_builder.build_post_processor(
options.midn_post_process)
self._oicr_post_process_fn = function_builder.build_post_processor(
options.oicr_post_process)
def __init__(self, model_proto, is_training=False):
"""Initializes the model.
Args:
model_proto: an instance of cam_model_pb2.CAMModel
is_training: if True, training graph will be built.
"""
super(Model, self).__init__(model_proto, is_training)
if not isinstance(model_proto, cam_model_pb2.CAMModel):
raise ValueError(
'The model_proto has to be an instance of CAMModel.')
self._vocabulary_list = model_utils.read_vocabulary(
model_proto.vocabulary_file)
tf.logging.info('Load %i classes: %s', len(self._vocabulary_list),
','.join(self._vocabulary_list))
self._input_scales = [1.0]
if len(model_proto.input_image_scale) > 0:
self._input_scales = [
scale for scale in model_proto.input_image_scale
]
self._cnn_feature_names = [model_proto.cnn_output_name]
if len(model_proto.cnn_feature_name) > 0:
self._cnn_feature_names = [
name for name in model_proto.cnn_feature_name
]
self._anchors = init_grid_anchors.initialize_grid_anchors(
stride_ratio=0.2)
def __init__(self, model_proto, is_training=False):
"""Initializes the model.
Args:
model_proto: an instance of nod5_model_pb2.NOD5Model
is_training: if True, training graph will be built.
"""
super(Model, self).__init__(model_proto, is_training)
if not isinstance(model_proto, nod5_model_pb2.NOD5Model):
raise ValueError('The model_proto has to be an instance of NOD5Model.')
options = model_proto
self._open_vocabulary_list = model_utils.read_vocabulary(
options.open_vocabulary_file)
with open(options.open_vocabulary_glove_file, 'rb') as fid:
self._open_vocabulary_initial_embedding = np.load(fid)
self._vocabulary_list = model_utils.read_vocabulary(options.vocabulary_file)
self._num_classes = len(self._vocabulary_list)
self._feature_extractor = build_faster_rcnn_feature_extractor(
options.feature_extractor, is_training,
options.inplace_batchnorm_update)
self._pcl_preprocess_fn = function_builder.build_post_processor(
options.pcl_preprocess)
self._midn_post_process_fn = function_builder.build_post_processor(
options.midn_post_process)
self._oicr_post_process_fn = function_builder.build_post_processor(
options.oicr_post_process)
self._text_encoding_fn = sequence_encoding.get_encode_fn(
options.text_encoding)
def __init__(self, model_proto, is_training=False):
"""Initializes the model.
Args:
model_proto: an instance of oicr_model_pb2.OICRModel
is_training: if True, training graph will be built.
"""
super(Model, self).__init__(model_proto, is_training)
if not isinstance(model_proto, oicr_model_pb2.OICRModel):
raise ValueError(
'The model_proto has to be an instance of OICRModel.')
self._vocabulary_list = model_utils.read_vocabulary(
model_proto.vocabulary_file)
self._num_classes = len(self._vocabulary_list)
def __init__(self, model_proto, is_training=False):
"""Initializes the model.
Args:
model_proto: an instance of visual_w2v_model_pb2.VisualW2vModel
is_training: if True, training graph will be built.
"""
super(Model, self).__init__(model_proto, is_training)
if not isinstance(model_proto, visual_w2v_model_pb2.VisualW2vModel):
raise ValueError(
'The model_proto has to be an instance of VisualW2vModel.')
options = model_proto
self._open_vocabulary_list = model_utils.read_vocabulary(
options.open_vocabulary_file)
with open(options.open_vocabulary_glove_file, 'rb') as fid:
self._open_vocabulary_initial_embedding = np.load(fid)
def __init__(self, model_proto, is_training=False):
"""Initializes the model.
Args:
model_proto: an instance of stacked_attn_model_pb2.StackedAttnModel
is_training: if True, training graph will be built.
"""
super(Model, self).__init__(model_proto, is_training)
if not isinstance(model_proto,
stacked_attn_model_pb2.StackedAttnModel):
raise ValueError(
'The model_proto has to be an instance of StackedAttnModel.')
options = model_proto
self._vocabulary_list = model_utils.read_vocabulary(
options.vocabulary_file)
self._num_classes = len(self._vocabulary_list)
self._feature_extractor = build_faster_rcnn_feature_extractor(
options.feature_extractor, is_training,
options.inplace_batchnorm_update)
def __init__(self, model_proto, is_training=False):
"""Initializes the model.
Args:
model_proto: an instance of reasoning_model_pb2.AdViSEGCN
is_training: if True, training graph will be built.
"""
super(Model, self).__init__(model_proto, is_training)
if not isinstance(model_proto, reasoning_model_pb2.ReasoningModel):
raise ValueError(
'The model_proto has to be an instance of AdViSEGCN.')
options = model_proto
# Read vocabulary.
def filter_fn(word_with_freq, min_freq):
return [word for word, freq in word_with_freq if freq >= min_freq]
stmt_vocab_with_freq = model_utils.read_vocabulary_with_frequency(
options.stmt_vocab_list_path)
stmt_vocab_list = filter_fn(stmt_vocab_with_freq, 5)
slgn_vocab_with_freq = model_utils.read_vocabulary_with_frequency(
options.slgn_vocab_list_path)
slgn_vocab_list = filter_fn(slgn_vocab_with_freq, 20)
slgn_dbpedia_vocab_with_freq = model_utils.read_vocabulary_with_frequency(
options.slgn_kb_vocab_list_path)
slgn_dbpedia_vocab_list = filter_fn(slgn_dbpedia_vocab_with_freq, 20)
ads_labels = model_utils.read_vocabulary(options.ads_vocab_list_path)
vocab_list = sorted(
set(stmt_vocab_list + slgn_vocab_list + slgn_dbpedia_vocab_list +
ads_labels))
tf.logging.info('Vocab, len=%i', len(vocab_list))
# Read glove data.
word2vec_dict, embedding_dims = {}, options.embedding_dims
if options.glove_path:
(word2vec_dict,
embedding_dims) = model_utils.load_glove_data(options.glove_path)
oov_word, glove_word, glove_vec = [], [], []
for word in vocab_list:
if not word in word2vec_dict:
oov_word.append(word)
else:
glove_word.append(word)
glove_vec.append(word2vec_dict[word])
self._embedding_dims = embedding_dims
self._shared_vocab = glove_word + oov_word + ['out-of-vocabulary']
if len(glove_vec) > 0:
self._glove_vec = np.stack(glove_vec, 0)
else:
self._glove_vec = np.zeros((0, embedding_dims))
tf.logging.info('Vocab, glove=%i, all=%i', len(glove_word),
len(self._shared_vocab))
self._shared_dims = options.shared_dims
# Text encoder.
self._text_encoder = sequence_encoder.build(options.text_encoder,
is_training=is_training)
# Graph creator.
self._graph_creator = graph_creator.build_graph_creator(
options.graph_creator, is_training)