def test_transforms_from_gin(self):
ds_builder = builder.DatasetBuilder()
batch = 32
ds = ds_builder.make('train', batch)
inputs, y_true, weights, _ = next(iter(ds))
self.assertIsInstance(inputs, tf.Tensor)
self.assertEqual(inputs.dtype, tf.int32)
self.assertIsInstance(y_true, dict)
self.assertGreater(len(y_true), 0)
self.assertIsInstance(weights, dict)
self.assertGreater(len(weights), 0)
self.assertEqual(y_true['alignments/0'].dtype, tf.int32)
self.assertEqual(y_true['alignments/0'].shape, (2 * batch, 3, 1024))
self.assertEqual(y_true['alignments/1'].dtype, tf.int32)
self.assertEqual(y_true['alignments/1'].shape, (2 * batch, 1))
self.assertEqual(weights['alignments/0'].dtype, tf.float32)
self.assertEqual(weights['alignments/0'].shape, (2 * batch, ))
self.assertEqual(weights['alignments/1'].dtype, tf.float32)
self.assertEqual(weights['alignments/1'].shape, (2 * batch, ))
def make_fake_builder(max_len = 512):
return builder.DatasetBuilder(
data_loader=FakePairsLoader(max_len=max_len),
labels=multi_task.Backbone(alignments=[
('alignment/targets', 'alignment/weights'),
'homology/targets']),
batched_transformations=[
transforms.Reshape(shape=(-1, max_len), on='sequence'),
transforms.Reshape(shape=(-1,), on='fam_key'),
align_transforms.CreateBatchedWeights(
on='alignment/targets', out='alignment/weights'),
align_transforms.PadNegativePairs(
on=['alignment/targets', 'alignment/weights']),
align_transforms.PadNegativePairs(
value=-1.0, on=['alignment/pid1', 'alignment/pid3']),
align_transforms.CreateHomologyTargets(
process_negatives=True, on='fam_key', out='homology/targets'),
],
metadata=('alignment/pid1', 'alignment/pid3'),
split='train')
def test_transforms_from_gin(self):
self.mock_load.return_value = make_fake_sequence_dataset()
ds_builder = builder.DatasetBuilder()
batch = 32
ds = ds_builder.make('test', batch)
inputs, y_true, weights, metadata = next(iter(ds))
self.assertIsInstance(inputs, tf.Tensor)
self.assertEqual(inputs.dtype, tf.int32)
self.assertIsInstance(y_true, dict)
self.assertGreater(len(y_true), 0)
self.assertIsInstance(weights, dict)
self.assertGreater(len(weights), 0)
for y in multi_task.Backbone.unflatten(y_true):
self.assertEqual(y.dtype, tf.int32)
self.assertEqual(y.shape, (batch, 1024)) # from gin.
for y in multi_task.Backbone.unflatten(weights):
self.assertEqual(y.dtype, tf.float32)
self.assertIn('seq_key', metadata)
self.assertIsInstance(metadata['seq_key'], tf.Tensor)
self.assertEqual(metadata['seq_key'].dtype, tf.int32)
self.assertNotIn('fam_key', metadata)
def _make_loop_with_reference(self):
"""Creates a training loop for alignments with self._loop as reference."""
seq_len = gin.query_parameter('%SEQUENCE_LENGTH')
model_cls = functools.partial(
dedal.Dedal,
encoder_cls=functools.partial(encoders.TransformerEncoder,
emb_dim=48,
num_layers=1,
num_heads=2,
mlp_dim=3 * seq_len,
max_len=seq_len),
aligner_cls=aligners.SoftAligner,
heads_cls=multi_task.Backbone(
embeddings=[], alignments=[homology.UncorrectedLogits]),
)
workdir2 = tempfile.mkdtemp()
ds_builder = builder.DatasetBuilder(labels=multi_task.Backbone(
alignments=[('target', 'weights')]))
return training_loop.TrainingLoop(
workdir=workdir2,
strategy=self._loop.strategy,
dataset_builder=ds_builder,
logger_cls=functools.partial(
logger.Logger,
scalars=multi_task.Backbone(
alignments=[[tf.keras.metrics.BinaryAccuracy]]),
every=5),
loss_fn=losses.MultiTaskLoss(losses=multi_task.Backbone(
alignments=[
tf.keras.losses.BinaryCrossentropy(
from_logits=True,
reduction=tf.keras.losses.Reduction.NONE)
])),
optimizer_cls=self._loop._optimizer_cls,
batch_size=self._loop._batch_size,
model_cls=model_cls,
num_steps=self._loop._num_steps,
reference_workdir=self._loop._workdir,
num_reference_steps=self._loop._num_steps)
def make_tape_builder(root_dir,
task,
target,
weights=None,
metadata=(),
max_len=1024,
input_sequence_key='primary',
output_sequence_key='sequence'):
"""Creates a DatasetBuilder for TAPE's benchmark."""
supported_tasks = list(TAPE_NUM_OUTPUTS)
if task not in supported_tasks:
raise ValueError(f'Task {task} not recognized.'
f'Supported tasks: {", ".join(supported_tasks)}.')
num_outputs = TAPE_NUM_OUTPUTS[task].get(target, 1)
used_keys = [input_sequence_key, target]
if weights is not None:
used_keys.append(weights)
if metadata:
used_keys.extend(metadata)
unused_keys = [k for k in TAPE_SPECS[task] if k not in used_keys]
ds_transformations = []
if max_len is not None:
ds_transformations.append(
transforms.FilterByLength(on=output_sequence_key,
precomputed=False,
max_len=max_len - 1))
transformations = [
transforms.Pop(on=unused_keys),
transforms.Reshape(on=output_sequence_key, shape=[]),
transforms.Encode(on=output_sequence_key),
transforms.EOS(on=output_sequence_key),
transforms.CropOrPad(on=output_sequence_key, size=max_len),
]
if target in TAPE_MULTI_CL_TASKS:
transformations.append(transforms.OneHot(on=target, depth=num_outputs))
elif target in TAPE_BACKBONE_ANGLE_TASKS:
transformations.append(transforms.BackboneAngleTransform(on=target))
elif target in TAPE_PROT_ENGINEERING_TASKS:
transformations.append(transforms.Reshape(on=target, shape=[-1]))
if target in TAPE_SEQ2SEQ_TASKS:
transformations.extend([
transforms.Reshape(on=target, shape=[-1, num_outputs]),
transforms.CropOrPadND(on=target, size=max_len, axis=0),
])
if weights is not None: # Note: no seq-level TAPE task has weights.
transformations.extend([
transforms.Reshape(on=weights, shape=[-1]),
transforms.CropOrPadND(on=weights, size=max_len),
])
embeddings_labels = [target] if weights is None else [(target, weights)]
return builder.DatasetBuilder(
data_loader=make_tape_loader(root_dir=root_dir, task=task),
ds_transformations=ds_transformations,
transformations=transformations,
labels=multi_task.Backbone(embeddings=embeddings_labels),
metadata=metadata,
sequence_key=output_sequence_key)
def make_pair_builder(max_len=512,
index_keys=('fam_key', 'ci_100'),
process_negatives=True,
gap_token='-',
sequence_key='sequence',
context_sequence_key='full_sequence',
loader_cls=make_pfam_pairs_loader,
pairing_cls=None,
lm_cls=None,
has_context=False,
append_eos=True,
append_eos_context=True,
add_random_tails=False,
**kwargs):
"""Creates a dataset for pairs of sequences."""
# Convenience function to index key pairs.
paired_keys = lambda k: tuple(f'{k}_{i}' for i in (1, 2))
def stack_and_pop(on):
stack = transforms.Stack(on=paired_keys(on), out=on)
pop = transforms.Pop(on=paired_keys(on))
return [stack, pop]
# Defines fields to be read from the TFRecords.
metadata_keys = ['cla_key', 'seq_key'] + list(index_keys)
extra_keys = metadata_keys.copy()
# Pre-paired datasets already been filtered by length, seq_len only needed
# when pairing sequences on-the-fly.
if pairing_cls is not None:
extra_keys.append('seq_len')
# Optionally, adds fields needed by the `AddAlignmentContext` `Transform`.
if has_context:
extra_keys.extend(['start', 'end'])
add_alignment_context_extra_args = (paired_keys(context_sequence_key) +
paired_keys('start') +
paired_keys('end'))
# Accounts for EOS token if necessary.
max_len_eos = max_len - 1 if append_eos else max_len
### Sets up the `DatasetTransform`s.
ds_transformations = []
if pairing_cls is not None:
filter_by_length = transforms.FilterByLength(max_len=max_len_eos)
# NOTE(fllinares): pairing on-the-fly is memory intensive on TPU for some
# reason not yet understood...
pair_sequences = pairing_cls(index_keys=index_keys)
ds_transformations.extend([filter_by_length, pair_sequences])
### Sets up the `Transform`s applied *before* batching.
project_msa_rows = align_transforms.ProjectMSARows(
on=paired_keys(sequence_key), token=gap_token)
append_eos_to_context = transforms.EOS(
on=paired_keys(context_sequence_key))
add_alignment_context = align_transforms.AddAlignmentContext(
on=paired_keys(sequence_key) + add_alignment_context_extra_args,
out=paired_keys(sequence_key),
max_len=max_len_eos,
gap_token=gap_token)
trim_alignment = align_transforms.TrimAlignment(
on=paired_keys(sequence_key), gap_token=gap_token)
pop_add_alignment_context_extra_args = transforms.Pop(
on=add_alignment_context_extra_args)
add_random_prefix_and_suffix = align_transforms.AddRandomTails(
on=paired_keys(sequence_key), max_len=max_len_eos)
create_alignment_targets = align_transforms.CreateAlignmentTargets(
on=paired_keys(sequence_key),
out='alignment/targets',
gap_token=gap_token,
n_prepend_tokens=0)
pid1 = align_transforms.PID(on=paired_keys(sequence_key),
out='alignment/pid1',
definition=1,
token=gap_token)
pid3 = align_transforms.PID(on=paired_keys(sequence_key),
out='alignment/pid3',
definition=3,
token=gap_token)
remove_gaps = transforms.RemoveTokens(on=paired_keys(sequence_key),
tokens=gap_token)
append_eos_to_sequence = transforms.EOS(on=paired_keys(sequence_key))
pad_sequences = transforms.CropOrPad(on=paired_keys(sequence_key),
size=max_len)
pad_alignment_targets = transforms.CropOrPadND(on='alignment/targets',
size=2 * max_len)
transformations = [project_msa_rows]
if has_context:
if append_eos_context:
transformations.append(append_eos_to_context)
transformations.extend([
add_alignment_context, trim_alignment,
pop_add_alignment_context_extra_args
])
if add_random_tails:
transformations.append(add_random_prefix_and_suffix)
transformations.append(create_alignment_targets)
transformations.extend([pid1, pid3, remove_gaps])
if append_eos:
transformations.append(append_eos_to_sequence)
transformations.extend([pad_sequences, pad_alignment_targets])
for key in [sequence_key] + metadata_keys:
transformations.extend(stack_and_pop(key))
### Sets up the `Transform`s applied *after* batching.
flatten_sequence_pairs = transforms.Reshape(on=sequence_key,
shape=[-1, max_len])
flatten_metadata_pairs = transforms.Reshape(on=metadata_keys, shape=[-1])
create_homology_targets = align_transforms.CreateHomologyTargets(
on='fam_key',
out='homology/targets',
process_negatives=process_negatives)
create_alignment_weights = align_transforms.CreateBatchedWeights(
on='alignment/targets', out='alignment/weights')
add_neg_alignment_targets_and_weights = align_transforms.PadNegativePairs(
on=('alignment/targets', 'alignment/weights'))
pad_neg_pid = align_transforms.PadNegativePairs(on=('alignment/pid1',
'alignment/pid3'),
value=-1.0)
batched_transformations = [
flatten_sequence_pairs, flatten_metadata_pairs, create_homology_targets
]
if process_negatives:
batched_transformations.extend([
create_alignment_weights, add_neg_alignment_targets_and_weights,
pad_neg_pid
])
if lm_cls is not None:
create_lm_targets = lm_cls(on=sequence_key,
out=(sequence_key, 'masked_lm/targets',
'masked_lm/weights'))
batched_transformations.append(create_lm_targets)
### Sets up the remainder of the `DatasetBuilder` configuration.
masked_lm_labels = ('masked_lm/targets', 'masked_lm/weights')
alignment_labels = ('alignment/targets' if not process_negatives else
('alignment/targets', 'alignment/weights'))
homology_labels = 'homology/targets'
embeddings = () if lm_cls is None else (masked_lm_labels, )
alignments = (alignment_labels, homology_labels)
return builder.DatasetBuilder(
data_loader=loader_cls(extra_keys),
ds_transformations=ds_transformations,
transformations=transformations,
batched_transformations=batched_transformations,
labels=multi_task.Backbone(embeddings=embeddings,
alignments=alignments),
metadata=('seq_key', 'alignment/pid1', 'alignment/pid3'),
sequence_key=sequence_key,
**kwargs)