def test_mx_pt_eq_training_data():
pytest.importorskip("mxnet")
from sockeye import data_io
train_line_count = 100
train_line_count_empty = 0
train_max_length = 30
dev_line_count = 20
dev_max_length = 30
test_line_count = 20
test_line_count_empty = 0
test_max_length = 30
batch_size = 5
with tmp_digits_dataset("tmp_corpus", train_line_count,
train_line_count_empty,
train_max_length - C.SPACE_FOR_XOS, dev_line_count,
dev_max_length - C.SPACE_FOR_XOS, test_line_count,
test_line_count_empty,
test_max_length - C.SPACE_FOR_XOS) as data:
vcb = vocab.build_from_paths(
[data['train_source'], data['train_target']])
train_iters = {}
val_iters = {}
# For each implementation
for key, data_io_module in (('mx', data_io), ('pt', data_io_pt)):
# Create iterators with no data permutation (preserve order for
# batch equality checks)
train_iter, val_iter, _, _ = data_io_module.get_training_data_iters(
sources=[data['train_source']],
targets=[data['train_target']],
validation_sources=[data['dev_source']],
validation_targets=[data['dev_target']],
source_vocabs=[vcb],
target_vocabs=[vcb],
source_vocab_paths=[None],
target_vocab_paths=[None],
shared_vocab=True,
batch_size=batch_size,
batch_type=C.BATCH_TYPE_SENTENCE,
max_seq_len_source=train_max_length,
max_seq_len_target=train_max_length,
bucketing=True,
bucket_width=10,
permute=False)
train_iters[key] = train_iter
val_iters[key] = val_iter
# Check equality of all MXNet/PyTorch batches
for iters in (train_iters, val_iters):
for mx_batch, pt_batch in zip(iters['mx'], iters['pt']):
_assert_mx_pt_batches_equal(mx_batch, pt_batch)
def test_get_training_data_iters():
train_line_count = 100
train_line_count_empty = 0
train_max_length = 30
dev_line_count = 20
dev_max_length = 30
expected_mean = 1.0
expected_std = 0.0
test_line_count = 20
test_line_count_empty = 0
test_max_length = 30
batch_size = 5
with tmp_digits_dataset("tmp_corpus", train_line_count,
train_line_count_empty,
train_max_length - C.SPACE_FOR_XOS, dev_line_count,
dev_max_length - C.SPACE_FOR_XOS, test_line_count,
test_line_count_empty,
test_max_length - C.SPACE_FOR_XOS) as data:
# tmp common vocab
vcb = vocab.build_from_paths(
[data['train_source'], data['train_target']])
train_iter, val_iter, config_data, data_info = data_io.get_training_data_iters(
sources=[data['train_source']],
target=data['train_target'],
validation_sources=[data['dev_source']],
validation_target=data['dev_target'],
source_vocabs=[vcb],
target_vocab=vcb,
source_vocab_paths=[None],
target_vocab_path=None,
shared_vocab=True,
batch_size=batch_size,
batch_by_words=False,
batch_num_devices=1,
max_seq_len_source=train_max_length,
max_seq_len_target=train_max_length,
bucketing=True,
bucket_width=10)
assert isinstance(train_iter, data_io.ParallelSampleIter)
assert isinstance(val_iter, data_io.ParallelSampleIter)
assert isinstance(config_data, data_io.DataConfig)
assert data_info.sources == [data['train_source']]
assert data_info.target == data['train_target']
assert data_info.source_vocabs == [None]
assert data_info.target_vocab is None
assert config_data.data_statistics.max_observed_len_source == train_max_length
assert config_data.data_statistics.max_observed_len_target == train_max_length
assert np.isclose(config_data.data_statistics.length_ratio_mean,
expected_mean)
assert np.isclose(config_data.data_statistics.length_ratio_std,
expected_std)
assert train_iter.batch_size == batch_size
assert val_iter.batch_size == batch_size
assert train_iter.default_bucket_key == (train_max_length,
train_max_length)
assert val_iter.default_bucket_key == (dev_max_length, dev_max_length)
assert train_iter.dtype == 'float32'
# test some batches
bos_id = vcb[C.BOS_SYMBOL]
eos_id = vcb[C.EOS_SYMBOL]
expected_first_target_symbols = np.full((batch_size, ),
bos_id,
dtype='float32')
for epoch in range(2):
while train_iter.iter_next():
batch = train_iter.next()
assert isinstance(batch, data_io.Batch)
source = batch.source.asnumpy()
target = batch.target.asnumpy()
label = batch.labels[C.TARGET_LABEL_NAME].asnumpy()
length_ratio_label = batch.labels[
C.LENRATIO_LABEL_NAME].asnumpy()
assert source.shape[0] == target.shape[0] == label.shape[
0] == batch_size
# target first symbol should be BOS
# each source sequence contains one EOS symbol
assert np.sum(source == eos_id) == batch_size
assert np.array_equal(target[:, 0],
expected_first_target_symbols)
# label first symbol should be 2nd target symbol
assert np.array_equal(label[:, 0], target[:, 1])
# each label sequence contains one EOS symbol
assert np.sum(label == eos_id) == batch_size
train_iter.reset()
def test_get_training_image_text_data_iters():
# Test images
source_list = ['1', '2', '3', '4', '100']
prefix = "tmp_corpus"
use_feature_loader = False
preload_features = False
train_max_length = 30
dev_max_length = 30
expected_mean = 1.0
expected_std = 1.0
test_max_length = 30
batch_size = 5
if use_feature_loader:
source_image_size = _FEATURE_SHAPE
else:
source_image_size = _CNN_INPUT_IMAGE_SHAPE
with tmp_img_captioning_dataset(source_list, prefix, train_max_length,
dev_max_length, test_max_length,
use_feature_loader) as data:
# tmp common vocab
vcb = vocab.build_from_paths([data['target'], data['target']])
train_iter, val_iter, config_data, data_info = data_io.get_training_image_text_data_iters(
source_root=data['work_dir'],
source=data['source'],
target=data['target'],
validation_source_root=data['work_dir'],
validation_source=data['validation_source'],
validation_target=data['validation_target'],
vocab_target=vcb,
vocab_target_path=None,
batch_size=batch_size,
batch_by_words=False,
batch_num_devices=1,
source_image_size=source_image_size,
fill_up="replicate",
max_seq_len_target=train_max_length,
bucketing=False,
bucket_width=10,
use_feature_loader=use_feature_loader,
preload_features=preload_features)
assert isinstance(train_iter, data_io.ParallelSampleIter)
assert isinstance(val_iter, data_io.ParallelSampleIter)
assert isinstance(config_data, data_io.DataConfig)
assert isinstance(data_info.sources[0], data_io.FileListReader)
assert data_info.target == data['target']
assert data_info.source_vocabs is None
assert data_info.target_vocab is None
assert config_data.data_statistics.max_observed_len_source == 0
assert config_data.data_statistics.max_observed_len_target == train_max_length - 1
assert np.isclose(config_data.data_statistics.length_ratio_mean,
expected_mean)
assert np.isclose(config_data.data_statistics.length_ratio_std,
expected_std)
assert train_iter.batch_size == batch_size
assert val_iter.batch_size == batch_size
assert train_iter.default_bucket_key == (0, train_max_length)
assert val_iter.default_bucket_key == (0, dev_max_length)
assert train_iter.dtype == 'float32'
# test some batches
bos_id = vcb[C.BOS_SYMBOL]
expected_first_target_symbols = np.full((batch_size, ),
bos_id,
dtype='float32')
for epoch in range(2):
while train_iter.iter_next():
batch = train_iter.next()
assert len(batch.data) == 2
assert len(batch.label) == 1
assert batch.bucket_key in train_iter.buckets
source = batch.data[0].asnumpy()
target = batch.data[1].asnumpy()
label = batch.label[0].asnumpy()
assert source.shape[0] == target.shape[0] == label.shape[
0] == batch_size
# target first symbol should be BOS
assert np.array_equal(target[:, 0],
expected_first_target_symbols)
# label first symbol should be 2nd target symbol
assert np.array_equal(label[:, 0], target[:, 1])
# each label sequence contains one EOS symbol
assert np.sum(label == vcb[C.EOS_SYMBOL]) == batch_size
train_iter.reset()
def test_get_training_data_iters():
train_line_count = 100
train_max_length = 30
dev_line_count = 20
dev_max_length = 30
expected_mean = 1.0
expected_std = 0.0
test_line_count = 20
test_line_count_empty = 0
test_max_length = 30
batch_size = 5
with tmp_digits_dataset("tmp_corpus",
train_line_count, train_max_length - C.SPACE_FOR_XOS,
dev_line_count, dev_max_length - C.SPACE_FOR_XOS,
test_line_count, test_line_count_empty,
test_max_length - C.SPACE_FOR_XOS) as data:
# tmp common vocab
vcb = vocab.build_from_paths([data['source'], data['target']])
train_iter, val_iter, config_data, data_info = data_io.get_training_data_iters(
sources=[data['source']],
target=data['target'],
validation_sources=[
data['validation_source']],
validation_target=data[
'validation_target'],
source_vocabs=[vcb],
target_vocab=vcb,
source_vocab_paths=[None],
target_vocab_path=None,
shared_vocab=True,
batch_size=batch_size,
batch_by_words=False,
batch_num_devices=1,
fill_up="replicate",
max_seq_len_source=train_max_length,
max_seq_len_target=train_max_length,
bucketing=True,
bucket_width=10)
assert isinstance(train_iter, data_io.ParallelSampleIter)
assert isinstance(val_iter, data_io.ParallelSampleIter)
assert isinstance(config_data, data_io.DataConfig)
assert data_info.sources == [data['source']]
assert data_info.target == data['target']
assert data_info.source_vocabs == [None]
assert data_info.target_vocab is None
assert config_data.data_statistics.max_observed_len_source == train_max_length
assert config_data.data_statistics.max_observed_len_target == train_max_length
assert np.isclose(config_data.data_statistics.length_ratio_mean, expected_mean)
assert np.isclose(config_data.data_statistics.length_ratio_std, expected_std)
assert train_iter.batch_size == batch_size
assert val_iter.batch_size == batch_size
assert train_iter.default_bucket_key == (train_max_length, train_max_length)
assert val_iter.default_bucket_key == (dev_max_length, dev_max_length)
assert train_iter.dtype == 'float32'
# test some batches
bos_id = vcb[C.BOS_SYMBOL]
eos_id = vcb[C.EOS_SYMBOL]
expected_first_target_symbols = np.full((batch_size,), bos_id, dtype='float32')
for epoch in range(2):
while train_iter.iter_next():
batch = train_iter.next()
assert len(batch.data) == 2
assert len(batch.label) == 1
assert batch.bucket_key in train_iter.buckets
source = batch.data[0].asnumpy()
target = batch.data[1].asnumpy()
label = batch.label[0].asnumpy()
assert source.shape[0] == target.shape[0] == label.shape[0] == batch_size
# target first symbol should be BOS
# each source sequence contains one EOS symbol
assert np.sum(source == eos_id) == batch_size
assert np.array_equal(target[:, 0], expected_first_target_symbols)
# label first symbol should be 2nd target symbol
assert np.array_equal(label[:, 0], target[:, 1])
# each label sequence contains one EOS symbol
assert np.sum(label == eos_id) == batch_size
train_iter.reset()
def test_get_training_data_iters():
train_line_count = 100
train_max_length = 30
dev_line_count = 20
dev_max_length = 30
expected_mean = 1.0
expected_std = 0.0
test_line_count = 20
test_line_count_empty = 0
test_max_length = 30
batch_size = 5
with tmp_digits_dataset("tmp_corpus",
train_line_count, train_max_length, dev_line_count, dev_max_length,
test_line_count, test_line_count_empty, test_max_length) as data:
# tmp common vocab
vcb = vocab.build_from_paths([data['source'], data['target']])
train_iter, val_iter, config_data = data_io.get_training_data_iters(data['source'], data['target'],
data['validation_source'],
data['validation_target'],
vocab_source=vcb,
vocab_target=vcb,
vocab_source_path=None,
vocab_target_path=None,
shared_vocab=True,
batch_size=batch_size,
batch_by_words=False,
batch_num_devices=1,
fill_up="replicate",
max_seq_len_source=train_max_length,
max_seq_len_target=train_max_length,
bucketing=True,
bucket_width=10)
assert isinstance(train_iter, data_io.ParallelSampleIter)
assert isinstance(val_iter, data_io.ParallelSampleIter)
assert isinstance(config_data, data_io.DataConfig)
assert config_data.source == data['source']
assert config_data.target == data['target']
assert config_data.vocab_source is None
assert config_data.vocab_target is None
assert config_data.data_statistics.max_observed_len_source == train_max_length - 1
assert config_data.data_statistics.max_observed_len_target == train_max_length
assert np.isclose(config_data.data_statistics.length_ratio_mean, expected_mean)
assert np.isclose(config_data.data_statistics.length_ratio_std, expected_std)
assert train_iter.batch_size == batch_size
assert val_iter.batch_size == batch_size
assert train_iter.default_bucket_key == (train_max_length, train_max_length)
assert val_iter.default_bucket_key == (dev_max_length, dev_max_length)
assert train_iter.dtype == 'float32'
# test some batches
bos_id = vcb[C.BOS_SYMBOL]
expected_first_target_symbols = np.full((batch_size,), bos_id, dtype='float32')
for epoch in range(2):
while train_iter.iter_next():
batch = train_iter.next()
assert len(batch.data) == 2
assert len(batch.label) == 1
assert batch.bucket_key in train_iter.buckets
source = batch.data[0].asnumpy()
target = batch.data[1].asnumpy()
label = batch.label[0].asnumpy()
assert source.shape[0] == target.shape[0] == label.shape[0] == batch_size
# target first symbol should be BOS
assert np.array_equal(target[:, 0], expected_first_target_symbols)
# label first symbol should be 2nd target symbol
assert np.array_equal(label[:, 0], target[:, 1])
# each label sequence contains one EOS symbol
assert np.sum(label == vcb[C.EOS_SYMBOL]) == batch_size
train_iter.reset()
def test_get_training_data_iters():
from sockeye.test_utils import tmp_digits_dataset
train_line_count = 100
train_line_count_empty = 0
train_max_length = 30
dev_line_count = 20
dev_max_length = 30
expected_mean = 1.0
expected_std = 0.0
test_line_count = 20
test_line_count_empty = 0
test_max_length = 30
batch_size = 5
num_source_factors = num_target_factors = 1
with tmp_digits_dataset("tmp_corpus", train_line_count,
train_line_count_empty,
train_max_length - C.SPACE_FOR_XOS, dev_line_count,
dev_max_length - C.SPACE_FOR_XOS, test_line_count,
test_line_count_empty,
test_max_length - C.SPACE_FOR_XOS) as data:
# tmp common vocab
vcb = vocab.build_from_paths(
[data['train_source'], data['train_target']])
train_iter, val_iter, config_data, data_info = data_io.get_training_data_iters(
sources=[data['train_source']],
targets=[data['train_target']],
validation_sources=[data['dev_source']],
validation_targets=[data['dev_target']],
source_vocabs=[vcb],
target_vocabs=[vcb],
source_vocab_paths=[None],
target_vocab_paths=[None],
shared_vocab=True,
batch_size=batch_size,
batch_type=C.BATCH_TYPE_SENTENCE,
max_seq_len_source=train_max_length,
max_seq_len_target=train_max_length,
bucketing=True,
bucket_width=10)
assert isinstance(train_iter, data_io.ParallelSampleIter)
assert isinstance(val_iter, data_io.ParallelSampleIter)
assert isinstance(config_data, data_io.DataConfig)
assert data_info.sources == [data['train_source']]
assert data_info.targets == [data['train_target']]
assert data_info.source_vocabs == [None]
assert data_info.target_vocabs == [None]
assert config_data.data_statistics.max_observed_len_source == train_max_length
assert config_data.data_statistics.max_observed_len_target == train_max_length
assert np.isclose(config_data.data_statistics.length_ratio_mean,
expected_mean)
assert np.isclose(config_data.data_statistics.length_ratio_std,
expected_std)
assert train_iter.batch_size == batch_size
assert val_iter.batch_size == batch_size
# test some batches
bos_id = vcb[C.BOS_SYMBOL]
eos_id = vcb[C.EOS_SYMBOL]
expected_first_target_symbols = torch.full((batch_size, 1),
bos_id,
dtype=torch.int32)
for epoch in range(2):
while train_iter.iter_next():
batch = train_iter.next()
assert isinstance(batch, data_io.Batch)
source = batch.source
target = batch.target
label = batch.labels[
C.
TARGET_LABEL_NAME] # TODO: still 2-shape: (batch, length)
length_ratio_label = batch.labels[C.LENRATIO_LABEL_NAME]
assert source.shape[0] == target.shape[0] == label.shape[
0] == batch_size
assert source.shape[2] == target.shape[
2] == num_source_factors == num_target_factors
# target first symbol should be BOS
# each source sequence contains one EOS symbol
assert torch.sum(source == eos_id) == batch_size
assert torch.equal(target[:, 0], expected_first_target_symbols)
# label first symbol should be 2nd target symbol
assert torch.equal(label[:, 0], target[:, 1, 0])
# each label sequence contains one EOS symbol
assert torch.sum(label == eos_id) == batch_size
train_iter.reset()
def test_mx_pt_eq_prepared_data():
pytest.importorskip("mxnet")
from sockeye import data_io
train_line_count = 100
train_line_count_empty = 0
train_max_length = 30
dev_line_count = 20
dev_max_length = 30
test_line_count = 20
test_line_count_empty = 0
test_max_length = 30
batch_size = 5
batch_sentences_multiple_of = 8
with tmp_digits_dataset("tmp_corpus", train_line_count,
train_line_count_empty,
train_max_length - C.SPACE_FOR_XOS, dev_line_count,
dev_max_length - C.SPACE_FOR_XOS, test_line_count,
test_line_count_empty,
test_max_length - C.SPACE_FOR_XOS) as data:
with TemporaryDirectory() as work_dir, utils.create_pool(2) as pool:
vcb = vocab.build_from_paths(
[data['train_source'], data['train_target']])
train_iters = {}
val_iters = {}
# For each implementation
for key, data_io_module in (('mx', data_io), ('pt', data_io_pt)):
output_folder = os.path.join(work_dir, key)
os.mkdir(output_folder)
# Create 1 shard (avoid random assignment that breaks equality)
shards, keep_tmp_shard_files = data_io_module.create_shards(
source_fnames=[data['train_source']],
target_fnames=[data['train_target']],
num_shards=1,
output_prefix=output_folder)
# Prepare data using multiple processes
data_io_module.prepare_data(
source_fnames=[data['train_source']],
target_fnames=[data['train_target']],
source_vocabs=[vcb],
target_vocabs=[vcb],
source_vocab_paths=[None],
target_vocab_paths=[None],
shared_vocab=True,
max_seq_len_source=train_max_length,
max_seq_len_target=train_max_length,
bucketing=True,
bucket_width=10,
num_shards=1,
output_prefix=output_folder,
bucket_scaling=True,
keep_tmp_shard_files=keep_tmp_shard_files,
pool=pool,
shards=shards)
# Create iterators
train_iter, val_iter, _, _, _ = data_io_module.get_prepared_data_iters(
prepared_data_dir=output_folder,
validation_sources=[data['dev_source']],
validation_targets=[data['dev_target']],
shared_vocab=True,
batch_size=batch_size,
batch_type=C.BATCH_TYPE_SENTENCE,
batch_sentences_multiple_of=batch_sentences_multiple_of,
permute=False)
train_iters[key] = train_iter
val_iters[key] = val_iter
# Check equality of all MXNet/PyTorch batches
for iters in (train_iters, val_iters):
for i, (mx_batch,
pt_batch) in enumerate(zip(iters['mx'], iters['pt']),
1):
print(i)
_assert_mx_pt_batches_equal(mx_batch, pt_batch)
def test_get_training_image_text_data_iters():
# Test images
source_list = ['1', '2', '3', '4', '100']
prefix = "tmp_corpus"
use_feature_loader = False
preload_features = False
train_max_length = 30
dev_max_length = 30
expected_mean = 1.0
expected_std = 1.0
test_max_length = 30
batch_size = 5
if use_feature_loader:
source_image_size = _FEATURE_SHAPE
else:
source_image_size = _CNN_INPUT_IMAGE_SHAPE
with tmp_img_captioning_dataset(source_list,
prefix,
train_max_length,
dev_max_length,
test_max_length,
use_feature_loader) as data:
# tmp common vocab
vcb = vocab.build_from_paths([data['target'], data['target']])
train_iter, val_iter, config_data, data_info = data_io.get_training_image_text_data_iters(source_root=data['work_dir'],
source=data['source'],
target=data['target'],
validation_source_root=data['work_dir'],
validation_source=data['validation_source'],
validation_target=data['validation_target'],
vocab_target=vcb,
vocab_target_path=None,
batch_size=batch_size,
batch_by_words=False,
batch_num_devices=1,
source_image_size=source_image_size,
fill_up="replicate",
max_seq_len_target=train_max_length,
bucketing=False,
bucket_width=10,
use_feature_loader=use_feature_loader,
preload_features=preload_features)
assert isinstance(train_iter, data_io.ParallelSampleIter)
assert isinstance(val_iter, data_io.ParallelSampleIter)
assert isinstance(config_data, data_io.DataConfig)
assert isinstance(data_info.sources[0], data_io.FileListReader)
assert data_info.target == data['target']
assert data_info.source_vocabs is None
assert data_info.target_vocab is None
assert config_data.data_statistics.max_observed_len_source == 0
assert config_data.data_statistics.max_observed_len_target == train_max_length - 1
assert np.isclose(config_data.data_statistics.length_ratio_mean, expected_mean)
assert np.isclose(config_data.data_statistics.length_ratio_std, expected_std)
assert train_iter.batch_size == batch_size
assert val_iter.batch_size == batch_size
assert train_iter.default_bucket_key == (0, train_max_length)
assert val_iter.default_bucket_key == (0, dev_max_length)
assert train_iter.dtype == 'float32'
# test some batches
bos_id = vcb[C.BOS_SYMBOL]
expected_first_target_symbols = np.full((batch_size,), bos_id, dtype='float32')
for epoch in range(2):
while train_iter.iter_next():
batch = train_iter.next()
assert len(batch.data) == 2
assert len(batch.label) == 1
assert batch.bucket_key in train_iter.buckets
source = batch.data[0].asnumpy()
target = batch.data[1].asnumpy()
label = batch.label[0].asnumpy()
assert source.shape[0] == target.shape[0] == label.shape[0] == batch_size
# target first symbol should be BOS
assert np.array_equal(target[:, 0], expected_first_target_symbols)
# label first symbol should be 2nd target symbol
assert np.array_equal(label[:, 0], target[:, 1])
# each label sequence contains one EOS symbol
assert np.sum(label == vcb[C.EOS_SYMBOL]) == batch_size
train_iter.reset()