123 lines
3.6 KiB
Python
123 lines
3.6 KiB
Python
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import numpy
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import pytest
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from thinc.api import reduce_first, reduce_last, reduce_max, reduce_mean, reduce_sum
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from thinc.types import Ragged
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@pytest.fixture
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def Xs():
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seqs = [numpy.zeros((10, 8), dtype="f"), numpy.zeros((4, 8), dtype="f")]
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for x in seqs:
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x[0] = 1
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x[1] = 2 # so max != first
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x[-1] = -1
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return seqs
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def test_init_reduce_first():
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model = reduce_first()
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def test_init_reduce_last():
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model = reduce_last()
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def test_init_reduce_mean():
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model = reduce_mean()
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def test_init_reduce_max():
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model = reduce_max()
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def test_init_reduce_sum():
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model = reduce_sum()
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def test_reduce_first(Xs):
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model = reduce_first()
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lengths = model.ops.asarray([x.shape[0] for x in Xs], dtype="i")
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X = Ragged(model.ops.flatten(Xs), lengths)
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Y, backprop = model(X, is_train=True)
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assert isinstance(Y, numpy.ndarray)
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assert Y.shape == (len(Xs), Xs[0].shape[1])
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assert Y.dtype == Xs[0].dtype
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assert list(Y[0]) == list(Xs[0][0])
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assert list(Y[1]) == list(Xs[1][0])
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dX = backprop(Y)
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assert dX.dataXd.shape == X.dataXd.shape
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def test_reduce_last(Xs):
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model = reduce_last()
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lengths = model.ops.asarray([x.shape[0] for x in Xs], dtype="i")
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X = Ragged(model.ops.flatten(Xs), lengths)
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Y, backprop = model(X, is_train=True)
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assert isinstance(Y, numpy.ndarray)
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assert Y.shape == (len(Xs), Xs[0].shape[1])
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assert Y.dtype == Xs[0].dtype
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assert list(Y[0]) == list(Xs[0][-1])
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assert list(Y[1]) == list(Xs[1][-1])
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dX = backprop(Y)
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assert dX.dataXd.shape == X.dataXd.shape
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def test_reduce_max(Xs):
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model = reduce_max()
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lengths = model.ops.asarray([x.shape[0] for x in Xs], dtype="i")
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X = Ragged(model.ops.flatten(Xs), lengths)
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Y, backprop = model(X, is_train=True)
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assert isinstance(Y, numpy.ndarray)
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assert Y.shape == (len(Xs), Xs[0].shape[1])
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assert Y.dtype == Xs[0].dtype
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assert list(Y[0]) == list(Xs[0][1])
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assert list(Y[1]) == list(Xs[1][1])
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dX = backprop(Y)
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assert dX.dataXd.shape == X.dataXd.shape
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def test_reduce_mean(Xs):
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Xs = [x * 1000 for x in Xs] # use large numbers for numeric stability
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model = reduce_mean()
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lengths = model.ops.asarray([x.shape[0] for x in Xs], dtype="i")
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X = Ragged(model.ops.flatten(Xs), lengths)
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Y, backprop = model(X, is_train=True)
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assert isinstance(Y, numpy.ndarray)
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assert Y.shape == (len(Xs), Xs[0].shape[1])
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assert Y.dtype == Xs[0].dtype
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assert numpy.all(Y[0] == Y[0][0]) # all values in row should be equal
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assert Y[0][0] == Xs[0].mean()
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assert numpy.all(Y[1] == Y[1][0])
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assert Y[1][0] == Xs[1].mean()
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dX = backprop(Y)
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assert dX.dataXd.shape == X.dataXd.shape
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def test_reduce_sum(Xs):
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model = reduce_sum()
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lengths = model.ops.asarray([x.shape[0] for x in Xs], dtype="i")
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X = Ragged(model.ops.flatten(Xs), lengths)
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Y, backprop = model(X, is_train=True)
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assert isinstance(Y, numpy.ndarray)
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assert Y.shape == (len(Xs), Xs[0].shape[1])
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assert Y.dtype == Xs[0].dtype
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assert Y[0][0] == Xs[0][:, 0].sum()
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assert numpy.all(Y[0] == Y[0][0])
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assert Y[1][-1] == Xs[1][:, 0].sum()
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assert numpy.all(Y[1] == Y[1][0])
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dX = backprop(Y)
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assert dX.dataXd.shape == X.dataXd.shape
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def test_size_mismatch(Xs):
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for reduce in [reduce_first, reduce_last, reduce_max, reduce_mean, reduce_sum]:
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model = reduce()
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lengths = model.ops.asarray([x.shape[0] for x in Xs], dtype="i")
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X = Ragged(model.ops.flatten(Xs), lengths)
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Y, backprop = model(X, is_train=True)
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Y_bad = Y[:-1]
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with pytest.raises(ValueError):
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backprop(Y_bad)
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