def make_activations(inputs, width):
"""Make an Activations object with the right sizing for an input."""
lh = numpy.ones((inputs.data.shape[0], width), dtype="f")
po = numpy.ones((len(inputs.lengths), width), dtype="f")
return Activations(
RaggedArray(lh, inputs.lengths),
RaggedArray(po, [1 for _ in inputs.lengths]))
def test_ragged_to_padded(extra_dims, lengths, pad_to, expected_shape):
arr = RaggedArray(numpy.ones((sum(lengths), ) + extra_dims), lengths)
if pad_to > 1 and pad_to < max(lengths):
with pytest.raises(ValueError):
padded = arr.to_padded(to=pad_to)
else:
padded = arr.to_padded(to=pad_to)
assert padded.shape == expected_shape
def test_with_length_batching(max_words, lengths, expected_batches, width=12):
inputs = RaggedArray(numpy.arange(sum(lengths), dtype="i"), lengths)
# The record_fwd and record_bwd variables will record the calls into the
# forward and backward passes, respectively. We can use this to check that
# the batching was done correctly.
dummy, record_fwd, record_bwd = create_dummy_model(width)
batched_dummy = with_length_batching(dummy, max_words)
outputs, backprop = batched_dummy.begin_update(inputs)
assert len(record_fwd) == len(expected_batches)
assert [b.lengths for b in record_fwd] == expected_batches
assert outputs.lh.data.shape == (inputs.data.shape[0], width)
assert outputs.po.data.shape == (len(inputs.lengths), width)
none = backprop(outputs)
assert none is None
assert len(record_bwd) == len(expected_batches)
def inputs(ids):
return RaggedArray(ids, [len(ids)])