def make_data_provider(self, **kwargs):
"""Creates DataProvider instance for this input pipeline. Additional keyword arguments
are passed to the DataProvider.
"""
context_keys_to_features = self._create_context_features()
sequence_keys_to_features = self._create_sequence_features()
items_to_handlers = self._create_items_to_handlers()
decoder = TFSequenceExampleDecoder(
context_keys_to_features=context_keys_to_features,
sequence_keys_to_features=sequence_keys_to_features,
items_to_handlers=items_to_handlers)
dataset = Dataset(
data_sources=self.data_files,
reader=tf.TFRecordReader,
decoder=decoder,
num_samples=self.meta_data.get('num_samples', {}).get(self.mode),
num_classes=self.meta_data.get('num_classes'),
items_to_descriptions=self.meta_data.get('items_to_descriptions',
{}),
meta_data=self.meta_data,
labels_to_names=self.meta_data.get('labels_to_classes'))
return DatasetDataProvider(dataset=dataset,
shuffle=self.shuffle,
num_epochs=self.num_epochs,
**kwargs)
def make_data_provider(self, **kwargs):
"""Creates DataProvider instance for this input pipeline. Additional keyword arguments
are passed to the DataProvider.
"""
splitter_source = SplitTokensDecoder(
tokens_feature_name='source_token',
length_feature_name='source_len',
append_token='SEQUENCE_END',
delimiter=self.source_delimiter)
splitter_target = SplitTokensDecoder(
tokens_feature_name='target_token',
length_feature_name='target_len',
prepend_token='SEQUENCE_START',
append_token='SEQUENCE_END',
delimiter=self.target_delimiter)
keys_to_features = {
self.source_field: tf.FixedLenFeature((), tf.string),
self.target_field: tf.FixedLenFeature((),
tf.string,
default_value="")
}
items_to_handlers = {
'source_token':
tfslim.tfexample_decoder.ItemHandlerCallback(
keys=[self.source_field],
func=lambda dict: splitter_source.decode(
dict[self.source_field], ['source_token'])[0]),
'source_len':
tfslim.tfexample_decoder.ItemHandlerCallback(
keys=[self.source_field],
func=lambda dict: splitter_source.decode(
dict[self.source_field], ['source_len'])[0]),
'target_token':
tfslim.tfexample_decoder.ItemHandlerCallback(
keys=[self.target_field],
func=lambda dict: splitter_target.decode(
dict[self.target_field], ['target_token'])[0]),
'target_len':
tfslim.tfexample_decoder.ItemHandlerCallback(
keys=[self.target_field],
func=lambda dict: splitter_target.decode(
dict[self.target_field], ['target_len'])[0])
}
decoder = TFExampleDecoder(keys_to_features, items_to_handlers)
dataset = Dataset(data_sources=self.files,
reader=tf.TFRecordReader,
decoder=decoder)
return DatasetDataProvider(dataset=dataset,
shuffle=self.shuffle,
num_epochs=self.num_epochs,
**kwargs)
def make_data_provider(self, **kwargs):
"""Creates DataProvider instance for this input pipeline. Additional keyword arguments
are passed to the DataProvider.
"""
keys_to_features = {
'image/encoded':
tf.FixedLenFeature((), tf.string, default_value=''),
'image/format':
tf.FixedLenFeature(
(),
tf.string,
default_value=self.meta_data.get('image_format')),
'image/class/label':
tf.FixedLenFeature([1],
tf.int64,
default_value=tf.zeros([1], dtype=tf.int64)),
}
image_shape = [
self.meta_data.get('height'),
self.meta_data.get('width'),
self.meta_data.get('channels')
]
if not all(image_shape):
# no reshaping should be done
image_shape = None
items_to_handlers = {
'image':
tfslim.tfexample_decoder.Image(
shape=image_shape, channels=self.meta_data.get('channels')),
'label':
tfslim.tfexample_decoder.Tensor('image/class/label', shape=[]),
}
decoder = TFExampleDecoder(keys_to_features, items_to_handlers)
dataset = Dataset(data_sources=self.data_files,
reader=tf.TFRecordReader,
decoder=decoder,
num_samples=self.meta_data.get('num_samples',
{}).get(self.mode),
num_classes=self.meta_data['num_classes'],
items_to_descriptions=self.meta_data.get(
'items_to_descriptions', {}),
meta_data=self.meta_data,
labels_to_names=self.meta_data['labels_to_classes'])
return DatasetDataProvider(dataset=dataset,
shuffle=self.shuffle,
num_epochs=self.num_epochs,
**kwargs)
def make_data_provider(self, **kwargs):
"""Creates DataProvider instance for this input pipeline. Additional keyword arguments
are passed to the DataProvider.
"""
context_keys_to_features = {
self.image_field:
tf.FixedLenFeature([], dtype=tf.string),
"image/format":
tf.FixedLenFeature([],
dtype=tf.string,
default_value=self.image_format),
}
sequence_keys_to_features = {
self.caption_ids_field:
tf.FixedLenSequenceFeature([], dtype=tf.int64),
self.caption_tokens_field:
tf.FixedLenSequenceFeature([], dtype=tf.string)
}
items_to_handlers = {
'image':
tfslim.tfexample_decoder.Image(image_key=self.image_field,
format_key="image/format",
channels=3),
'target_ids':
tfslim.tfexample_decoder.Tensor(self.caption_ids_field),
'target_token':
tfslim.tfexample_decoder.Tensor(self.caption_tokens_field),
'target_len':
tfslim.tfexample_decoder.ItemHandlerCallback(
keys=[self.caption_tokens_field],
func=lambda x: tf.size(x[self.caption_tokens_field]))
}
decoder = TFSequenceExampleDecoder(context_keys_to_features,
sequence_keys_to_features,
items_to_handlers)
dataset = Dataset(data_sources=self.files,
reader=tf.TFRecordReader,
decoder=decoder,
num_samples=None,
items_to_descriptions={})
return DatasetDataProvider(dataset=dataset,
shuffle=self.shuffle,
num_epochs=self.num_epochs,
**kwargs)