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