1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
|
import pytest
import numpy
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)
|
