def test_cache():
dataset = IterableDataset(range(100))
stream = DataStream(dataset)
batched_stream = Batch(stream, ConstantScheme(11))
cached_stream = Cache(batched_stream, ConstantScheme(7))
epoch = cached_stream.get_epoch_iterator()
# Make sure that cache is filled as expected
for (features,), cache_size in zip(epoch, [4, 8, 1, 5, 9, 2,
6, 10, 3, 7, 0, 4]):
assert len(cached_stream.cache[0]) == cache_size
# Make sure that the epoch finishes correctly
for (features,) in cached_stream.get_epoch_iterator():
pass
assert len(features) == 100 % 7
assert not cached_stream.cache[0]
# Ensure that the epoch transition is correct
cached_stream = Cache(batched_stream, ConstantScheme(7, times=3))
for _, epoch in zip(range(2), cached_stream.iterate_epochs()):
cache_sizes = [4, 8, 1]
for i, (features,) in enumerate(epoch):
assert len(cached_stream.cache[0]) == cache_sizes[i]
assert len(features) == 7
assert numpy.all(list(range(100))[i * 7:(i + 1) * 7] == features)
assert i == 2
def test_epoch_transition(self):
cached_stream = Cache(self.stream, ConstantScheme(7, times=3))
for _, epoch in zip(range(2), cached_stream.iterate_epochs()):
cache_sizes = [4, 8, 1]
for i, (features,) in enumerate(epoch):
assert_equal(len(cached_stream.cache[0]), cache_sizes[i])
assert_equal(len(features), 7)
assert_equal(list(range(100))[i * 7:(i + 1) * 7], features)
assert_equal(i, 2)
def test_epoch_transition(self):
cached_stream = Cache(self.stream, ConstantScheme(7, times=3))
for _, epoch in zip(range(2), cached_stream.iterate_epochs()):
cache_sizes = [4, 8, 1]
for i, (features,) in enumerate(epoch):
assert_equal(len(cached_stream.cache[0]), cache_sizes[i])
assert_equal(len(features), 7)
assert_equal(list(range(100))[i * 7:(i + 1) * 7], features)
assert_equal(i, 2)
def test_epoch_finishes_correctly(self):
cached_stream = Cache(self.stream, ConstantScheme(7))
data = list(cached_stream.get_epoch_iterator())
assert_equal(len(data[-1][0]), 100 % 7)
assert not cached_stream.cache[0]
stream = Batch(DataStream(IterableDataset(range(3000))),
ConstantScheme(3200))
cached_stream = Cache(stream, ConstantScheme(64))
data = list(cached_stream.get_epoch_iterator())
assert_equal(len(data[-1][0]), 3000 % 64)
assert not cached_stream.cache[0]
def test_cache():
dataset = IterableDataset(range(100))
stream = DataStream(dataset)
batched_stream = Batch(stream, ConstantScheme(11))
cached_stream = Cache(batched_stream, ConstantScheme(7))
epoch = cached_stream.get_epoch_iterator()
# Make sure that cache is filled as expected
for (features, ), cache_size in zip(epoch,
[4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 0, 4]):
assert len(cached_stream.cache[0]) == cache_size
# Make sure that the epoch finishes correctly
for (features, ) in cached_stream.get_epoch_iterator():
pass
assert len(features) == 100 % 7
assert not cached_stream.cache[0]
# Ensure that the epoch transition is correct
cached_stream = Cache(batched_stream, ConstantScheme(7, times=3))
for _, epoch in zip(range(2), cached_stream.iterate_epochs()):
cache_sizes = [4, 8, 1]
for i, (features, ) in enumerate(epoch):
assert len(cached_stream.cache[0]) == cache_sizes[i]
assert len(features) == 7
assert numpy.all(list(range(100))[i * 7:(i + 1) * 7] == features)
assert i == 2
def test_epoch_finishes_correctly(self):
cached_stream = Cache(self.stream, ConstantScheme(7))
data = list(cached_stream.get_epoch_iterator())
assert_equal(len(data[-1][0]), 100 % 7)
assert not cached_stream.cache[0]
stream = Batch(DataStream(IterableDataset(range(3000))),
ConstantScheme(3200))
cached_stream = Cache(stream, ConstantScheme(64))
data = list(cached_stream.get_epoch_iterator())
assert_equal(len(data[-1][0]), 3000 % 64)
assert not cached_stream.cache[0]
def test_epoch_finishes_correctly(self):
cached_stream = Cache(self.stream, ConstantScheme(7))
data = list(cached_stream.get_epoch_iterator())
assert_equal(len(data[-1][0]), 100 % 7)
assert not cached_stream.cache[0]
def test_cache_fills_correctly(self):
cached_stream = Cache(self.stream, ConstantScheme(7))
epoch = cached_stream.get_epoch_iterator()
for (features,), cache_size in zip(epoch, [4, 8, 1, 5, 9, 2,
6, 10, 3, 7, 0, 4]):
assert_equal(len(cached_stream.cache[0]), cache_size)
def test_axis_labels_passed_on_by_default(self):
self.stream.axis_labels = {'features': ('batch', 'index')}
cached_stream = Cache(self.stream, ConstantScheme(7))
assert_equal(cached_stream.axis_labels, self.stream.axis_labels)
def test_value_error_on_none_request(self):
cached_stream = Cache(self.stream, ConstantScheme(7))
cached_stream.get_epoch_iterator()
assert_raises(ValueError, cached_stream.get_data, None)
def test_value_error_on_none_request(self):
cached_stream = Cache(self.stream, ConstantScheme(7))
cached_stream.get_epoch_iterator()
assert_raises(ValueError, cached_stream.get_data, None)
def test_epoch_finishes_correctly(self):
cached_stream = Cache(self.stream, ConstantScheme(7))
data = list(cached_stream.get_epoch_iterator())
assert_equal(len(data[-1][0]), 100 % 7)
assert not cached_stream.cache[0]
def test_cache_fills_correctly(self):
cached_stream = Cache(self.stream, ConstantScheme(7))
epoch = cached_stream.get_epoch_iterator()
for (features,), cache_size in zip(epoch, [4, 8, 1, 5, 9, 2,
6, 10, 3, 7, 0, 4]):
assert_equal(len(cached_stream.cache[0]), cache_size)
def get_stream(source,
target,
source_input_dict,
target_label_dict,
batch_size,
buffer_multiplier=100,
input_token_level='word',
n_input_tokens=0,
n_labels=0,
reverse_labels=False,
max_input_length=None,
max_label_length=None,
pad_labels=True,
is_sort=True):
"""Returns a stream over sentence pairs.
Parameters
----------
source : list
A list of files to read source languages from.
target : list
A list of corresponding files in the target language.
source_word_dict : str
Path to a tab-delimited text file whose last column contains the
vocabulary.
target_label_dict : str
See `source_char_dict`.
batch_size : int
The minibatch size.
buffer_multiplier : int
The number of batches to load, concatenate, sort by length of
source sentence, and split again; this makes batches more uniform
in their sentence length and hence more computationally efficient.
n_source_words : int
The number of words in the source vocabulary. Pass 0 (default) to
use the entire vocabulary.
n_target_labels : int
See `n_chars_source`.
"""
if len(source) != len(target):
raise ValueError("number of source and target files don't match")
# Read the dictionaries
dicts = [
load_dict(source_input_dict, dict_size=n_input_tokens),
load_dict(target_label_dict,
dict_size=n_labels,
reverse=reverse_labels,
include_unk=False)
]
# Open the two sets of files and merge them
streams = [
TextFile(source,
dicts[0],
level=input_token_level,
bos_token=None,
eos_token=EOS_TOKEN,
encoding='utf-8').get_example_stream(),
TextFile(target,
dicts[1],
level='word',
bos_token=None,
unk_token=None,
eos_token=EOS_TOKEN,
encoding='utf-8').get_example_stream()
]
merged = Merge(streams, ('source_input_tokens', 'target_labels'))
if reverse_labels:
merged = SortLabels(merged)
# Filter sentence lengths
if max_input_length or max_label_length:
def filter_pair(pair):
src_input_tokens, trg_labels = pair
src_input_ok = (not max_input_length) or \
len(src_input_tokens) <= (max_input_length + 1)
trg_label_ok = (not max_label_length) or \
len(trg_labels) <= (max_label_length + 1)
return src_input_ok and trg_label_ok
merged = Filter(merged, filter_pair)
# Batches of approximately uniform size
large_batches = Batch(merged,
iteration_scheme=ConstantScheme(batch_size *
buffer_multiplier))
# sorted_batches = Mapping(large_batches, SortMapping(_source_length))
# batches = Cache(sorted_batches, ConstantScheme(batch_size))
# shuffled_batches = Shuffle(batches, buffer_multiplier)
# masked_batches = Padding(shuffled_batches,
# mask_sources=('source_chars', 'target_labels'))
if is_sort:
sorted_batches = Mapping(large_batches, SortMapping(_source_length))
else:
sorted_batches = large_batches
batches = Cache(sorted_batches, ConstantScheme(batch_size))
mask_sources = ('source_input_tokens', 'target_labels')
masked_batches = Padding(batches, mask_sources=mask_sources)
return masked_batches
def get_stream(source,
target,
source_dict,
target_dict,
batch_size,
buffer_multiplier=100,
n_words_source=0,
n_words_target=0,
max_src_length=None,
max_trg_length=None):
"""Returns a stream over sentence pairs.
Parameters
----------
source : list
A list of files to read source languages from.
target : list
A list of corresponding files in the target language.
source_dict : str
Path to a tab-delimited text file whose last column contains the
vocabulary.
target_dict : str
See `source_dict`.
batch_size : int
The minibatch size.
buffer_multiplier : int
The number of batches to load, concatenate, sort by length of
source sentence, and split again; this makes batches more uniform
in their sentence length and hence more computationally efficient.
n_words_source : int
The number of words in the source vocabulary. Pass 0 (default) to
use the entire vocabulary.
n_words_target : int
See `n_words_source`.
"""
if len(source) != len(target):
raise ValueError("number of source and target files don't match")
# Read the dictionaries
dicts = [
load_dict(source_dict, n_words=n_words_source),
load_dict(target_dict, n_words=n_words_target)
]
# Open the two sets of files and merge them
streams = [
TextFile(source, dicts[0], bos_token=None,
eos_token=EOS_TOKEN).get_example_stream(),
TextFile(target, dicts[1], bos_token=None,
eos_token=EOS_TOKEN).get_example_stream()
]
merged = Merge(streams, ('source', 'target'))
# Filter sentence lengths
if max_src_length or max_trg_length:
def filter_pair(pair):
src, trg = pair
src_ok = (not max_src_length) or len(src) < max_src_length
trg_ok = (not max_trg_length) or len(trg) < max_trg_length
return src_ok and trg_ok
merged = Filter(merged, filter_pair)
# Batches of approximately uniform size
large_batches = Batch(merged,
iteration_scheme=ConstantScheme(batch_size *
buffer_multiplier))
sorted_batches = Mapping(large_batches, SortMapping(_source_length))
batches = Cache(sorted_batches, ConstantScheme(batch_size))
shuffled_batches = Shuffle(batches, buffer_multiplier)
masked_batches = Padding(shuffled_batches)
return masked_batches
