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import numpy
import pytest
from numpy.testing import assert_allclose
from thinc.api import (
Dropout,
Linear,
Model,
NumpyOps,
add,
clone,
concatenate,
map_list,
noop,
)
from thinc.layers import chain, tuplify
@pytest.fixture(params=[1, 2, 9])
def nB(request):
return request.param
@pytest.fixture(params=[1, 6])
def nI(request):
return request.param
@pytest.fixture(params=[1, 5, 3])
def nH(request):
return request.param
@pytest.fixture(params=[1, 2, 7, 9])
def nO(request):
return request.param
@pytest.fixture
def model1(nH, nI):
return Linear(nH, nI)
@pytest.fixture
def model2(nO, nH):
return Linear(nO, nH)
@pytest.fixture
def model3(nO):
return Linear(nO, nO)
def test_tuplify_zero():
with pytest.raises(TypeError):
tuplify()
def test_tuplify_one(model1):
with pytest.raises(TypeError):
tuplify(model1)
def test_tuplify_two(model1, model2):
model = tuplify(model1, model2)
assert len(model.layers) == 2
def test_tuplify_operator_two(model1, model2):
with Model.define_operators({"&": tuplify}):
model = model1 & model2
assert len(model.layers) == 2
def test_tuplify_dulicates_input():
model = tuplify(noop(), noop())
ones = numpy.ones([10])
out = model.predict(ones)
assert out == (ones, ones)
def test_tuplify_initialize(nI, nO):
linear = Linear(nO)
model = tuplify(linear, linear)
ones = numpy.ones((1, nI), dtype="float")
model.initialize(X=ones)
def test_tuplify_three(model1, model2, model3):
model = tuplify(model1, model2, model3)
assert len(model.layers) == 3
def test_tuplify_operator_three(model1, model2, model3):
# Previously we 'flattened' these nested calls. We might opt to do so
# again, especially for the operators.
with Model.define_operators({"&": tuplify}):
model = model1 & model2 & model3
assert len(model.layers) == 2
assert len(model.layers[0].layers) == 2
def test_chain_zero():
with pytest.raises(TypeError):
chain()
def test_chain_one(model1):
with pytest.raises(TypeError):
chain(model1)
def test_chain_two(model1, model2):
model = chain(model1, model2)
assert len(model.layers) == 2
def test_chain_operator_two(model1, model2):
with Model.define_operators({">>": chain}):
model = model1 >> model2
assert len(model.layers) == 2
def test_chain_three(model1, model2, model3):
model = chain(model1, model2, model3)
assert len(model.layers) == 3
def test_chain_operator_three(model1, model2, model3):
# Previously we 'flattened' these nested calls. We might opt to do so
# again, especially for the operators.
with Model.define_operators({">>": chain}):
model = model1 >> model2 >> model3
assert len(model.layers) == 2
assert len(model.layers[0].layers) == 2
def test_chain_right_branch(model1, model2, model3):
# Previously we 'flattened' these nested calls. We might opt to do so
# again, especially for the operators.
merge1 = chain(model1, model2)
merge2 = chain(merge1, model3)
assert len(merge1.layers) == 2
assert len(merge2.layers) == 2
@pytest.mark.parametrize("ops", [NumpyOps(), NumpyOps(use_blis=True)])
def test_chain(ops):
data = numpy.asarray([[1, 2, 3, 4]], dtype="f")
model = chain(Linear(1), Dropout(), Linear(1))
model.ops = ops
model.initialize(data, data)
Y, backprop = model(data, is_train=True)
backprop(Y)
# Layers with and without nO/nI
model = chain(Linear(1), Dropout(), Linear(1, 1))
model.initialize(data, data)
# Setting dim on model
model = chain(Linear(1), Dropout(), Linear(1))
model.set_dim("nO", 1)
model.initialize(data, None)
model = chain(Linear(1, 1), Dropout(), Linear(1, 1))
model.set_dim("nI", 1)
model.initialize(None, data)
# Not enough arguments
with pytest.raises(TypeError):
chain(Linear())
with pytest.raises(TypeError):
chain()
def test_concatenate_one(model1):
model = concatenate(model1)
assert isinstance(model, Model)
def test_concatenate_two(model1, model2):
model = concatenate(model1, model2)
assert len(model.layers) == 2
def test_concatenate_operator_two(model1, model2):
with Model.define_operators({"|": concatenate}):
model = model1 | model2
assert len(model.layers) == 2
def test_concatenate_three(model1, model2, model3):
model = concatenate(model1, model2, model3)
assert len(model.layers) == 3
def test_concatenate_operator_three(model1, model2, model3):
with Model.define_operators({"|": concatenate}):
model = model1 | model2 | model3
assert len(model.layers) == 3
def test_clone_changes_predictions(nH, nI):
model1 = Linear(nH)
model = clone(model1, 10)
ones = numpy.ones((10, nI), dtype="f")
model.initialize(X=ones)
output_from_cloned = model.predict(ones)
output_from_orig = model1.predict(ones)
assert output_from_cloned.sum() != output_from_orig.sum()
def test_clone_gives_distinct_ids(nH, nI):
model = clone(Linear(nH), 5)
assert len(model.layers) == 5
seen_ids = set()
for node in model.walk():
assert node.id not in seen_ids
seen_ids.add(node.id)
assert len(seen_ids) == 6
def test_clone_noop():
model = clone(Linear(), 0)
assert len(model.layers) == 0
assert model.name == "noop"
def test_concatenate_noop():
model = concatenate()
assert len(model.layers) == 0
assert model.name == "noop"
def test_noop():
data = numpy.asarray([1, 2, 3], dtype="f")
model = noop(Linear(), Linear())
model.initialize(data, data)
Y, backprop = model(data, is_train=True)
assert numpy.array_equal(Y, data)
dX = backprop(Y)
assert numpy.array_equal(dX, data)
def test_add():
data = numpy.asarray([[1, 2, 3, 4]], dtype="f")
model = add(Linear(), Linear())
model.initialize(data, data)
Y, backprop = model(data, is_train=True)
Y2 = sum(layer.predict(data) for layer in model.layers)
assert numpy.array_equal(Y, Y2)
dX = backprop(Y)
assert dX.shape == data.shape
# Test that nesting works
model2 = add(model, Linear())
assert len(model2.layers) == 3
model.initialize(data, data)
Y = model2.predict(data)
Y2 = sum(layer.predict(data) for layer in model2.layers)
assert numpy.array_equal(Y, Y2)
def test_add_edge_cases():
data = numpy.asarray([[1, 2, 3, 4]], dtype="f")
with pytest.raises(TypeError):
add()
model = add(Linear(), Linear())
model._layers = []
Y, backprop = model(data, is_train=True)
assert numpy.array_equal(data, Y)
dX = backprop(Y)
assert numpy.array_equal(dX, data)
def test_concatenate():
data = numpy.asarray([[1, 2, 3], [4, 5, 6]], dtype="f")
model = concatenate(Linear(), Linear())
model.initialize(data, data)
Y, backprop = model(data, is_train=True)
assert Y.shape[1] == sum([layer.predict(data).shape[1] for layer in model.layers])
dX = backprop(Y)
assert dX.shape == data.shape
def test_map_list():
nI = 4
nO = 9
Xs = [numpy.zeros((6, nI), dtype="f"), numpy.ones((3, nI), dtype="f")]
Y_shapes = [(x.shape[0], nO) for x in Xs]
model = map_list(Linear())
model.initialize(X=Xs, Y=[numpy.zeros(shape, dtype="f") for shape in Y_shapes])
Ys, backprop = model(Xs, is_train=True)
assert isinstance(Ys, list)
assert len(Ys) == len(Xs)
layer = model.layers[0]
for X, Y in zip(Xs, Ys):
assert_allclose(layer.predict(X), Y)
dXs = backprop(Ys)
assert isinstance(dXs, list)
assert len(dXs) == len(Xs)
assert dXs[0].shape == Xs[0].shape
assert dXs[1].shape == Xs[1].shape
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